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AAUW deeply appreciates the following generous contributors for their support of this research report.AAUW gratefully acknowledges the financial support of the National ScienceFoundation, Research on Gender in Science and Engineering award 1420214, forthe production and dissemination of this report. Any opinions, findings, and conclusionsor recommendations expressed in this material are those of the authors anddo not necessarily reflect the views of the National Science Foundation.AAUW gratefully acknowledges AT&T, the exclusive Information andCommunications Technology Member of AAUW’s STEM Workforce Coalition.AT&T’s leadership support will help the report inspire maximum impact amongthe broadest audience possible. The views expressed in this report do notnecessarily reflect the views of AT&T.AAUW gratefully acknowledges the members of the Mooneen Lecce GivingCircle for their generous support and encouragement of AAUW’s research for thepast eight years. Their work honors the legacy of Mooneen Lecce, whose passion forAAUW’s mission continues to inspire volunteerism and charitable giving dedicatedto improving the lives of women and girls.

ContentsForewordAcknowledgmentsExecutive SummaryChapter 1. Women in Engineering and ComputingChapter 2. Why So Few?Chapter 3. Gender Bias and EvaluationsChapter 4. Gender Bias and Self-ConceptsChapter 5. Stereotype Threat in the WorkplaceChapter 6. Making the World a Better PlaceChapter 7. College Environment and CurriculumChapter 8. Persistence and Sense of FitChapter 9. A Workplace for EveryoneChapter 10. What Can We Do?AppendixReferencesixxi17334755636975859197111128FiguresFigure 1. Women in Selected STEM Occupations, 1960–2013Figure 2. Engineering Workforce, by Gender and Race/Ethnicity, 2006–2010Figure 3. Computing Workforce, by Gender and Race/Ethnicity, 2006–2010Figure 4. Women in Engineering, Computing, and Selected Other Occupations, 2013Figure 5. Intent of First-Year College Students to Major in STEM Fields,by Gender, 2014Figure 6. Bachelor’s Degrees Earned by Women, Selected Fields, 1970–2013Figure 7.Figure 8.Figure 9.Figure 10.Bachelor’s Degrees Awarded in Engineering, by Disciplineand Gender, 2013Bachelor’s Degrees Awarded in Computing, by Genderand Discipline, 2013Population Ages 20–24 and Bachelor’s Degrees Awarded in Selected Fields,by Race/Ethnicity and Gender, 2013Student Population from High School to Doctorate in Engineering orComputing, by Race/Ethnicity and Gender, 2012Figure 11. Retention in Engineering, by Gender, 2010Figure 12. Retention in Computing, by Gender, 2010999141819202123242727

Figure 13. Female Faculty, by Rank and Discipline, 2004 and 2013Figure 14.Figure 15.Figure 16.Figure 17.Figure 18.Figure 19.Figure 20.Figure 21.Figure 22.Figure 23.Figure 24.Figure 25.Faculty Ratings of Lab Manager Applicant, by Gender of ApplicantProbability of Selecting the Best Candidate for a Mathematical TaskAverage Science-Male Implicit Association Test Score,by Gender and College MajorAverage Science-Male Explicit Association Score, by Genderand College MajorResearch Conversations with Male Colleagues and Level of JobDisengagement, by GenderSocial Conversations with Male Colleagues and Level of JobDisengagement, by GenderPerceived Communal and Agentic Goal Fulfillment, by Type of CareerCareer Interest as a Function of Endorsement of Communal GoalsAverage Goal Endorsement, by GenderFemale Computer Science Graduates Nationally and at Harvey MuddCollege, by Graduation Year, 2000–2014Factors Contributing to Harvey Mudd College Students’ Choice to Majorin Computer Science, by GenderFemale Engineers’ Experience of Incivility at Work, by Level ofJob Satisfaction28495257596666717272778194Appendix FiguresFigure A1.Figure A2.Engineering OccupationsComputing OccupationsFigure A3. Pay Gap in Selected Engineering and Computing Occupations, 2013Figure A4. Associate Degrees Earned by Women, Selected Fields, 1990–2013Figure A5.Engineering and Computing Bachelor’s Degrees, by Race/Ethnicityand Gender, 2013Figure A6a. Engineering Bachelor’s Degrees Awarded to Women at the 25 LargestU.S. Engineering Schools, 2012Figure A6b. Engineering Bachelor’s Degrees Awarded to Women by U.S. EngineeringSchools with the Highest Representation of Women, 2012Figure A6c. Computing Bachelor’s Degrees Awarded to Women at the 25 LargestU.S. Computing Schools, 2012Figure A6d. Computing Bachelor’s Degrees Awarded to Women at U.S. ComputingSchools with the Highest Representation of Women, 2012Figure A7. Computing Occupations, Current (2012) and Projected (2022)Employment112112113114115116117118119120

AAUWixForewordDuring the 2014 White House Science Fair, President Barack Obama used a sportsmetaphor to explain why we must address the shortage of women in science, technology,engineering, and mathematics (STEM), particularly in the engineering and computingfields: “Half our team, we’re not even putting on the field. We’ve got to change thosenumbers.”AAUW has been a leader in efforts to promote women in STEM since our founding in1881. Through fellowships, programs, advocacy, and research, AAUW has inspired hundredsof thousands of girls to pursue science and math and helped thousands of womenbecome scientists, engineers, and mathematicians. AAUW’s 2010 research report, WhySo Few? Women in Science, Technology, Engineering, and Mathematics, sparked nationwideinterest in the shortage of women in STEM, leading to new initiatives in schools, colleges,and government. Indeed, significant progress has been made in fields such as biologyand chemistry; yet in engineering and computing, women remain a distinct minority.Solving the Equation: The Variables for Women’s Success in Engineering and Computingfocuses on the underrepresentation of women in engineering and computing and providespractical ideas for educators and employers seeking to foster gender diversity. From newways of conceptualizing the fields for beginning students to good management practices,the report recommends large and small actions that can add up to real change.Engineering and computing are too important for women to be less than fully represented.Diversity in these fields can contribute to creativity and productivity and, ultimately,to greater innovation. Join AAUW in our efforts to empower women and girls tosucceed in every field of endeavor.Patricia Fae HoAAUW PresidentLinda D. Hallman, CAEAAUW Executive Director

Executive Summary

AAUW 3also offered a higher salary to the male candidate.Another study highlighted in chapter 3 found thatpotential employers systematically underestimatedthe mathematical performance of women comparedwith men, resulting in the hiring of lowerperformingmen over higher-performing womenfor mathematical work. Once objective past-performanceinformation was introduced, however, theemployers made less biased hiring decisions. Biasis prevalent, but its effects can be diminished withmore comprehensive information.Hundreds of studies have documented thepower of stereotypes to influence performancethrough a phenomenon known as “stereotypethreat.” Stereotype threat occurs when individualsfear that they will confirm a negative stereotypeabout a group to which they belong. One suchgroup is “women.” When negative stereotypesabout women’s mathematical abilities are broughtto test-takers’ attention during tests, women’s performancedrops. Stereotype threat has been theorizednot only to influence women’s mathematicalperformance but also to contribute to disengagementfrom fields in which women are negativelystereotyped, such as engineering and computing.Much research has been done on how stereotypethreat can affect academic performance, butresearchers are only recently beginning to examinehow stereotype threat affects women in theworkplace. One finding in this area, highlightedin chapter 5, showed that the more often femaleSTEM faculty had research-related conversationswith their male colleagues, the less engaged theyfelt with their work. In contrast, the more socialconversations female STEM faculty had with theirmale colleagues, the more engaged they reportedbeing with their work. One possible explanationfor this finding is that research-related conversationswith male colleagues may generate stereotypethreat for female scientists. Social conversationswith male colleagues, on the other hand, may lessenthe threat by increasing a feeling of belonging intheir work environment. Research suggests that stereotypesare activated for women more frequentlywhen few women work in an organization. Thepresence of women at all levels of an organizationhas the potential to create environments that areless threatening for women.Gender biases affect not only how we view andtreat others but also how we view ourselves andwhat actions we take as a result. From early childhoodwe are exposed to stereotypes that guide ourchoices and behavior in powerful and often invisibleways, steering us toward certain careers andaway from others. As early as first grade, childrenhave already developed implicit biases associatingmath with boys. Studies suggest that girls whomore strongly associate math with boys and menare less likely to perceive themselves as being interestedin or skilled at math and less likely to spendtime studying or engaging with math concepts.A recent analysis of international differencesin the composition of engineering and computingfields makes clear that the surrounding culturemakes a difference in the gender makeup of thesefields. Women in the United States earn approximatelya fifth of all computing degrees, whereas inMalaysia women earn about half of all computingdegrees. Similarly, in the United States womenearn fewer than a fifth of engineering degrees.In Indonesia, however, women earn almost halfof engineering degrees, and in a diverse groupof countries women account for about a third ofrecent engineering graduates.A study described in chapter 4 finds that mostmen who major in engineering and computing haverelatively strong implicit biases associating menwith science, whereas their female counterparts tendto have relatively weak science-male implicit biases.Engineering and computing workplaces have awider gap in the gender-science bias among femaleand male employees relative to other fields. Femalerole models in engineering and computing can helpshift implicit biases for both women and men.Emphasizing socialrelevanceOne factor that may contribute to girls and womenchoosing to pursue fields other than engineeringand computing is the small but well-documentedgender difference in desire to work with and help

4SOLVING THE EQUATIONother people. Although communal goals are widelyvalued by both women and men, research describedin chapter 6 finds that women are more likely thanmen to prioritize helping and working with otherpeople over other career goals. Engineering andcomputing jobs clearly can provide opportunitiesfor fulfilling communal goals, but jobs in thesefields are not generally viewed that way. Rather,engineering and computing are often thought ofas solitary occupations that offer few opportunitiesfor social contribution. The perception and, in somecases, the reality that engineering and computingoccupations lack opportunities to work with andhelp others may in part explain the underrepresentationof women in these fields. Incorporatingcommunal aspects—both in messaging and in substance—intoengineering and computing work willlikely increase the appeal of these fields to communallyoriented people, many of whom are women.Cultivating a senseof belongingPerhaps because of this combination of stereotypes,biases, and values, women often report thatthey don’t feel as if they belong in engineering andcomputing fields. A study highlighted in chapter 8found that female engineering students were lesslikely than their male counterparts to feel a strongsense of fit with the idea of “being an engineer”as early as their first year in college. This moretenuous sense of fit with the professional role of anengineer was found to be associated with a greaterlikelihood of leaving the field. By emphasizing thewide variety of expertise necessary to be a successfulengineer or computing professional—includingless stereotypically masculine skills such as writing,communicating, and organizing—college engineeringand computing programs can help youngwomen see engineering and computing as fields inwhich they belong.Changing the environmentPast decades have shown that simply trying torecruit girls and women into existing engineeringand computing educational programs andworkplaces has had limited success. Changing theenvironment in college and the workplace appearsto be a prerequisite for fully integrating womeninto these fields.CollegeHarvey Mudd College is a prime example of howchanging structures and environments can resultin a dramatic increase in women’s representationin computing. With leadership from the collegepresident and college-wide support, Harvey Muddincreased the percentage of women graduatingfrom its computing program from 12 percentto approximately 40 percent in five years. Thisdramatic increase was accomplished through threemajor changes: revising the introductory computingcourse and splitting it into two levels dividedby experience, providing research opportunitiesfor undergraduates after their first year in college,and taking female students to the Grace HopperCelebration of Women in Computing conference.These changes can be modified and applied at othercolleges and universities. Taken together, they providea roadmap for reversing the downward trendin women’s representation among bachelor’s degreerecipients in computing.The workplaceWhile many studies have focused on factorscontributing to women entering STEM occupations,far fewer have looked at the arguably equallyimportant question of why women leave thesefields, often after years of preparation, and whatfactors support them in staying. The research featuredin chapter 9 sheds light on why some womenleave the engineering workforce and why othersstay. Women who leave engineering are very similarto women who stay in engineering. The differencesthe researchers found were not in the womenthemselves but in their workplace environments.Women who left engineering were less likelyto have opportunities for training and development,support from co-workers or supervisors, andsupport for balancing work and nonwork roles thanwere women who stayed in the profession. Femaleengineers who were most satisfied with their jobs,

in contrast, worked for organizations that providedclear paths for advancement, gave employeeschallenging assignments that helped develop andstrengthen new skills, and valued and recognizedemployees’ contributions.Women are making significant contributions tothe fields of engineering and computing yet are stilla distinct minority in these fields. Stereotypes andbiases lie at the core of the challenges facing womenin engineering and computing. Educational andworkplace environments are dissuading women whomight otherwise succeed in these fields. Expandingwomen’s representation in engineering and computingwill require concerted effort by employers, educationalinstitutions, policy makers, and individualsto create environments that are truly welcoming forwomen.AAUW 5

Chapter 1.Women in Engineeringand Computing

8SOLVING THE EQUATIONIn recent years the fields of science, technology,engineering, and mathematics—collectively knownas STEM—have received much attention for theircritical role in maintaining our nation’s competitiveedge in the global economy. Although the STEMfields are often grouped together, important differencesexist among them. In particular, engineeringand computing stand out as the STEM fields thatoffer the best opportunities for the greatest numberof people, accounting for more than 80 percentof STEM jobs (Landivar, 2013) as well as offeringa higher return on educational investment. Yetwomen remain less well represented in engineeringthan in any other STEM field, 1 and computing hasthe dubious distinction of being the only STEMfield in which women’s representation has steadilydeclined throughout the past few decades. In 2013just 12 percent of engineers and 26 percent of computingprofessionals were women (AAUW analysisof U.S. Department of Labor, Bureau of LaborStatistics, 2014b). Black and Hispanic women areeven more underrepresented compared with theirrepresentation in the general population than arewomen overall, making up just 4 percent of computingprofessionals and fewer than 2 percent ofengineers (AAUW analysis of U.S. Census Bureau,2011a).This set of circumstances motivated AAUW toexamine the latest research on the factors behindthe persistent underrepresentation of womenin the U.S. engineering and computing workforce.AAUW chose to focus on engineering andcomputing together because of important commonalitiesbetween these two fields, including thequantitative nature of the work and the masculineculture in both fields. Reviewing research on theunderrepresentation of women in engineeringand computing allowed AAUW to draw on alarger body of research that is often relevant forboth fields. This chapter explores the dimensionsof women’s underrepresentation in these fields,chapter 2 discusses the reasons behind women’sunderrepresentation, chapters 3 through 9 describespecific research findings in detail, and chapter 10provides recommendations for change. Overall thereport aims to increase understanding of women’sunderrepresentation and suggest ways in whichwomen’s participation can be increased.Women’s persistentunderrepresentation inengineering and computingIn the past 50 years women have entered theworkforce in record numbers, making their wayinto many fields previously dominated by men. Butwomen’s gains have been more modest in engineeringand computing than in other historically maleprofessions, such as law, business, and medicine.Women made up just 3 percent of lawyers andjudges in 1960, but women’s representation inthose jobs had increased to 33 percent by 2013.Likewise among physicians and surgeons, womenmade up just 7 percent of professionals in 1960, butthat number had grown to 36 percent by 2013. Inmanagement occupations women held 14 percentof jobs in 1960 and 38 percent in 2013 (AAUWanalysis of U.S. Census Bureau, 1963, and U.S.Department of Labor, Bureau of Labor Statistics,2014d).Women have also made substantial gains inmany STEM disciplines. For instance, womenmade up 8 percent of chemists in 1960 butaccounted for 39 percent of the chemistry workforceby 2013 (see figure 1). In biology, women’srepresentation increased from just over a quarter tojust over half of the workforce during this period.On the other hand, women’s representation incomputing declined from just over a third of workersin 1990 to just over a quarter in 2013, aboutthe same as it was in 1960. In engineering, womenmade up less than 1 percent of workers in 1960,growing to 12 percent of the workforce by 2013.Indeed, engineering has been described as the mostsex-segregated nonmilitary profession in the world(Cech, Rubineau et al., 2011; Charles & Bradley,2009).Figure 2 shows that, in recent years, whitewomen have made up 8 percent, Asian and PacificIslander women have made up 2 percent, and blackand Hispanic women have each made up 1 percentof the U.S. engineering workforce (see figure A1 inthe appendix for the list of occupations includedin the engineering workforce). Overall, for bothwomen and men, more than three-fourths ofengineering workers in the United States werenon-Hispanic white, 13 percent were Asian and

10SOLVING THE EQUATIONPacific Islander, 5 percent were Hispanic, and 4percent were black (some of these percentages donot match figure 2 exactly because of roundingcorrections).Figure 3 shows that in computing, whitewomen made up 17 percent, Asian and PacificIslander women made up 4 percent, black womenmade up 3 percent, and Hispanic women made up1 percent of the workforce (see figure A2 in theappendix for the list of occupations included incomputing). Overall, for both women and men, 69percent of computing workers were non-Hispanicwhites, 17 percent were Asian and Pacific Islander,7 percent were black, and 6 percent were Hispanic(some of these percentages do not match figure 3exactly because of rounding corrections).Diversity in high-techcompaniesSome technology companies have for years madepublic the gender and racial/ethnic makeup oftheir workforce (see Intel Corporation, 2014, forexample). Other prominent companies, such asGoogle, Apple, and Facebook, recently made publicfor the first time the racial/ethnic diversity of theiremployees as well as the percentage of their technicalemployees who are women. With 17 percent ofits technical workforce made up of women, Google(2014a) freely admitted, “We are not where wewant to be when it comes to diversity.” Apple (2014)reported that women account for 20 percent of itstechnical workforce. At Facebook (2014), womenmake up just 15 percent of technical workers, and atTwitter (2014) only 10 percent of technical workersare women. Other prominent high-tech companiesreport similar numbers. These recent efforts towardtransparency are an important step in addressingthe problem and tracking future progress.Why women’srepresentation mattersWhy does women’s representation in engineeringand computing fields matter? The answer can besummed up in one word: innovation. Finding solutionsto many of the big problems of this century,including climate change, universal access to water,disease, and renewable energy, will require the skillsof engineers and computer scientists. When womenare not well represented in these fields, everyonemisses out on the novel solutions that diverseparticipation brings. Moreover, a recent experimentshowed that lower-performing men are frequentlyselected over higher-performing women for mathematicalwork (Reuben et al., 2014a). With so fewwomen working in these fields, U.S. engineeringand technology companies are losing out on a massivetalent pool and are less globally competitivethan they could be because they may not be hiringthe best people for the jobs.In addition, when women are so dramaticallyunderrepresented, many technical decisions arebased on the experiences, opinions, and judgmentsof only men (Williams, 2014), and needs uniqueto women may be overlooked. Schiebinger andSchraudner (2011) call for including sex and genderin all phases of research to avoid costly retrofitsand spur innovation. As Margolis and Fisher (2002,pp. 2–3) point out,Some early voice-recognition systems werecalibrated to typical male voices. As a result,women’s voices were literally unheard. …Similar cases are found in many otherindustries. For instance, a predominantlymale group of engineers tailored the firstgeneration of automotive airbags to adultmale bodies, resulting in avoidable deaths forwomen and children.The diversity advantageUsually defined in terms of race, ethnicity, gender,disability status, age, or sexual orientation,diversity is a popular topic in the business community.Business leaders’ current attention todiversity is rooted in the civil rights laws of the1960s. In the 1980s and 1990s, some companies

AAUW 11A higher return on a college degreeEngineering and computing typically offer a higherreturn on educational investment than do other STEMfields. In general, engineering and computing jobs areless likely than other scientific jobs to require educationbeyond a bachelor’s degree. Only 1 percent of jobsin engineering and computing fields, 2 compared withjust under 30 percent of science and math jobs, 3 requiremore education than a bachelor’s degree for an entrylevelposition. This means that engineering and computingoccupations require less investment of time andmoney in education compared with many other STEMoccupations. At the same time, engineering and computingjobs tend to pay better than other STEM jobs. Infact, nearly all of the highest-paid STEM workers—94percent of workers in the 25 highest-paying STEM occupations—areengineers or computing professionals(AAUW analysis of U.S. Department of Labor, Bureau ofLabor Statistics, 2014e).Job opportunities are also more numerous in engineeringand computing than in other STEM fields, numberingapproximately 5.5 million jobs in 2013. Because engineeringand computing jobs make up 80 percent of theSTEM labor market (Landivar, 2013) and computing jobsin particular are predicted to grow dramatically over thenext decade, many more engineering and computingjobs than other types of STEM jobs are expected to beavailable in the near future.began promoting tolerance and multiculturalismin the workplace, having recognized the need toadvance working relationships among co-workersof diverse backgrounds (Anand & Winters, 2008).More recently, discussions about diversity focus onenhancing business performance (Catalyst, 2013).Diversity is widely linked to positive outcomes,such as greater innovation and productivity. Acommon theme among researchers boils down tothe advantages of combining ideas among individualsin a group. Indeed, scholars have found thatdiverse people working together can outperformthe “lone genius with a high IQ” (Page, 2007). Onestudy found that gender diversity contributed tothe “collective intelligence” of the group (Woolleyet al., 2010). The intellectual process of resolvingdifferences itself is seen as important. Thoughpeople often feel more comfortable with others likethemselves, homogeneity can hamper the exchangeof different ideas (Phillips et al., 2009). Studies specificallyfocusing on gender diversity have demonstrateda strong connection with corporate performance(Catalyst, 2004, 2011). Numerous studieshave connected a higher representation of womenat all levels of organizations, from board membersto employees, with better outcomes (NationalCenter for Women and Information Technology,2014b).Of course, diversity does not always resultin better outcomes. Diversity can have negativeeffects, such as decreased cooperation, reducedcohesiveness, and increased turnover, largely stemmingfrom social categorization—in other words,an “us-versus-them” mentality (Roberge & vanDick, 2010). One study of women’s representationon executive boards in the United States found thatdiversity could inhibit or propel strategic change,depending on the economic circumstances of thecompany (Triana et al., 2013).While the benefits of diversity are not automatic,the rewards are attainable (Campbell etal., 2013). To achieve a benefit, researchers callfor a “diversity mindset,” defined as learning andbuilding from diversity (van Knippenberg et al.,2013). A diversity mindset is especially valuable forwork involving exploration, creativity, and complexthinking. By adopting a diversity mindset, organizationalleaders can help cement the positive effectsof diversity and minimize the negative effects byimplementing fair employment practices and integratingexcluded groups into the workplace culture(Nishii, 2013).

12SOLVING THE EQUATIONHigh-quality jobsIn addition to encouraging innovation, increasingwomen’s representation in engineering and computingalso promotes gender equity. Both fieldsoffer good salaries. Especially among workerswithout graduate and professional degrees, earningsin engineering and computing outpace other fields(Carnevale et al., 2011). The average starting salaryfor individuals with a bachelor’s degree in engineeringor computing was approximately $62,000in 2014, tens of thousands of dollars higherthan annual salaries among other recent collegegraduates (National Association of Colleges andEmployers, 2014; AAUW, 2012). When womenaren’t well represented in these fields, womenand their families miss out on the good salariesthat engineering and computing occupations canprovide.Workplace flexibilitySome evidence shows that engineering and computingjobs tend to offer more-flexible hours andwork locations than many other jobs. A recentanalysis found that engineering, technology, andscience occupations offer greater time flexibility andmore independence in determining tasks, priorities,and goals compared with business, health, andlaw professions (Goldin, 2014). Another analysisfound that women in STEM fields, predominantlyinformation technology and engineering, workedslightly fewer hours than did women in otherprofessional occupations, such as management,financial operations, and nursing, and were morelikely to have flexible schedules than those otherprofessionals had (Glass et al., 2013).Another indicator of flexibility is the optionto work from home. Computer and engineeringworkers are increasingly likely—and more likelythan workers in many other fields—to work fromhome. The U.S. Census Bureau (Mateyka et al.,2012) reports that in 2010, more than 400,000computing, engineering, and science workers usuallyworked from home, a 69 percent increase since2000—and a faster rate of growth than seen in anyother occupational field. The only workers whowere more likely to work from home were those inmanagement, business, and financial fields. In additionto workers who usually work from home, manymore work from home some of the time. 4Job satisfactionMany people appear to find engineering and computingjobs to be very satisfying. In a recent analysisof 25,000 volunteer respondents, CareerBliss(2014), a job listings website focused on helpingworkers find “happiness in the workplace,” reportedthat the “happiest job” of 2014 (out of 169 jobs)was database administrator and the second happiestjob was quality assurance engineer. All told, thissurvey found that four of the top 10 happiest jobswere engineering or computing jobs.The gender pay gapFinally, while women in engineering and computing,like women in virtually all occupations, are paidless than men, the gender gap in earnings tends tobe substantially narrower in engineering and computingoccupations than in the overall labor force.In the overall population of full-time workers, atypical woman is paid 78 cents for every dollar paidto a typical man (U.S. Census Bureau, 2014b). Infields such as mechanical engineering and computerprogramming, women are paid more than90 cents for every dollar paid to men for full-timework. In engineering and many other occupations,this gap is largely associated with seniority, withlittle gap among early-career engineers, a slightgap for mid-career, and a larger gap for late-careerengineers (Frehill, 2011; AAUW, 2014). See figureA3 in the appendix for more information.EngineeringEngineers design, build, and test products.Merriam-Webster defines engineering as “theapplication of science and mathematics by whichthe properties of matter and the sources of energyin nature are made useful to people.” Branchesof engineering include aerospace, biomedical,chemical, civil, electrical, environmental, industrial,mechanical, and systems, and within thesebranches, subdisciplines exist. 5

AAUW 13As Vassar, Bryn Mawr, and other women’s collegesbegan teaching physics, biology, and chemistryin the late 1800s, the idea of an engineeringdepartment for female students was never considered.A handful of women earned U.S. engineeringdegrees in the late 1800s and early 1900s, but theyremained outliers (Bix, 2014). By the 1950s womenmade up fewer than 1 percent of engineeringstudents in colleges and universities. Changing thissituation required all-male engineering programsto admit women, which institutions began to doduring World War II and through the 1960s. Evenafter women were officially allowed into engineeringprograms, however, historic ties to male-dominatedapprenticeships and powerful cultural normsmarked engineering as “male.” As Bix argues,“Women’s engineering ambitions were of a moredeeply transgressive nature (than women’s scientificambitions) because technical knowledge—withits ties to industry, heavy manual labor, and themilitary—was a far more masculine domain thanscience.” As an indication of just how masculinethe field of engineering has been, one review foundthat in 1920, “the U.S. Census Bureau reported thatthe 41 women who were enumerated as engineerswere most likely mistaken about their jobs” (Frehill,2004, p. 384).While men make up the majority of all typesof engineers, some engineering fields are moremale-dominated than others. For example, womenmake up just 6 to 8 percent of petroleum, mechanical,and electrical and electronics engineers. Inindustrial, biomedical, and environmental engineering,on the other hand, women make up 17 to 21percent of working engineers (see figure 4).Engineering for boys, home economics for girlsHistorically, many engineering-minded women wereencouraged to study home economics rather than engineering(Bix, 2002). Ellen Swallow Richards, the founderof the American Home Economics Association (as well asa co-founder of the Association of Collegiate Alumnae,the predecessor organization to AAUW), was trained asa chemist at Vassar and MIT (Stage, 1997) and conductedimportant water-quality research that led to the firstmodern U.S. municipal sewage treatment plant.Describing the history of the development of the homeeconomics discipline, Alice Pawley (2012, p. 63) wrote,The founders of home economics wantedwomen to apply the logic of scientific managementto domestic contexts to develop better,more effective, and more efficient ways ofoperating the home. Improved health, hygiene,and sanitation, improved knowledge of nutrition,more efficient technologies for lighting,heating, and cleaning, and management techniquesfor supervising servants and raisingchildren, all organized around the home, constitutedthe realm of a new, science-orientedunderstanding of the domestic sphere, createdand maintained by women. What is crucialabout this history with respect to the constructionof engineering is the realization thatthe actual tasks awarded to home economicscould easily have been considered “science” or“engineering” tasks had they been in a differentcontext.According to one analysis of the study of home economicsat Iowa State University, engineering majorsand home economics majors learned very similar conceptsbut related them to different applications (Bix,2002). Whereas agricultural engineering majors tookapart and inspected tractors, home economics majorsdisassembled and evaluated stoves. While mechanicalengineering majors learned the thermodynamics behinddiesel engines, home economics majors learned aboutthe physics of refrigeration. This historical context ofintentional segregation of women and men into differentfields, despite the similar technical underpinnings oftheir work, helps explain why progress in increasing therepresentation of women in engineering has been slow.

14SOLVING THE EQUATIONFIGURE 4. WOMEN IN ENGINEERING, COMPUTING, AND SELECTED OTHER OCCUPATIONS, 2013Petroleum6%Electrical and electronics8%Mechanical8%Aerospace9%OTHER PROFESSIONALS COMPUTING PROFESSIONALS ENGINEERSComputer hardwareCivilMining and geological,including mining safetyEngineers, all otherChemicalMaterialsIndustrial, includinghealth and safetyBiomedicalEnvironmentalComputer network architectsNetwork and computersystems administratorsInformation security analystsSoftware developers,applications and systemsComputer and informationresearch scientistsComputer occupations, all otherComputer programmersComputer support specialistsDatabase administratorsComputer systems analystsWeb developersLawyersPhysicians and surgeonsChemists and materials scientistsBiological scientistsSecondary school teachersMedical scientists11%11%13%13%14%16%17%20%21%7%18%19%20%20%23%24%28%32%Overall U.S. full-time workforce: 44% women36%39%35%36%39%50%55%56%Registered nurses0 20 40 60 80 100Percentage of full-time professionals who are womenNotes: Occupations are self-reported. All occupations designated as computer and engineering occupations by the U.S. Department of Labor, Bureau of Labor Statistics, thatemployed at least 500 men and 500 women in 2013 are shown. Occupations shown in “other professionals” are selected professions shown for reference.Source: L. M. Frehill analysis of data from U.S. Department of Labor, Bureau of Labor Statistics (2014c).89%

AAUW 15ComputingLike engineering, computing is all around us,influencing how we work, play, and communicate.Computing professionals work with computersystems, including hardware, software, and networkingtechnology. They design and producehardware components such as computer chips andhard drives, design computer systems and networks,develop and test software, perform maintenance ontechnology, provide support to users of informationtechnology products, and design video games. 6Computing professionals are employed not onlyby technology companies but across nearly everysector of the workforce. In the modern world, inwhich society depends on technology and computers,most workplaces employ computing professionalsin some form.Women’s representation in computing has takena different path than women’s representation inany other field. While today fewer than one in fivecomputer science degrees is awarded to a woman,and women make up around a quarter of thecomputing workforce, women were not always sosparsely represented in computing.Women were a significant presence in the earlydecades of computing. Ada Lovelace, consideredthe first computer programmer, was an early pioneerwho lived in the mid-1800s. Women made upthe majority of programmers during World WarII (Abbate, 2012). Kay McNulty, Betty Snyder,Marlyn Wescoff, Ruth Lichterman, Jean Jennings,and Frances Bilas were the first programmers of thefirst programmable electronic computer, ENIAC(Electronic Numerical Integrator and Computer).Graduates of a University of Pennsylvania course totrain women to perform ballistics calculations, thewomen were originally hired as “subprofessional”workers but were later promoted to professionalstatus as a result of their innovative work in developingprogramming procedures for the ENIAC,which consisted of 18,000 vacuum tubes (Abbate,2012; Light, 1999). Rear Admiral Grace Hopper,another early pioneer, created the first compiler in1952.With a few notable exceptions, however, womenworking in computing in the mid-1900s wereviewed as low-status clerical employees. Computeroperators inherited their positions and status fromthe clerical, administrative, data-processing work ofkey punch operators, as well as from women whohad been employed as “human computers” to docalculations by hand (Misa, 2010; Abbate, 2012;Schlombs, 2010). Koput and Gutek (2010, p. 92)describe women moving into the computer andinformation technology sector, often in “basementjobs” in “the bowels of large corporations.” Thesejobs were not part of any career ladders; therefore,workers were not well compensated and were usuallyoverlooked for career advancement.Throughout the mid-1900s, computing wasstill a new field that lacked a gender identity andneeded workers, and it attracted women as well asmen (Ensmenger, 2010b; Koput & Gutek, 2010;Abbate, 2012). Yet, as Koput and Gutek (2010, p.103) wrote, “Over a relatively short period of time,a field that was once relatively gender integratedhas become solidly male dominated.” Computinghistorians and researchers have proposed a numberof explanations, including hiring practices in the1960s and 1970s that favored men, the increasingprofessionalization of the field, a stronger connectionwith the technical engineering culture, amale gaming culture that entered computing withthe rise of the personal computer, and increasinglystringent requirements for being admitted toan undergraduate computing program due to theincreasing popularity of the field.Ensmenger (2010b) found that companiesin the 1960s and 1970s looking for potentialcomputer programmers often used aptitude andpersonality tests that privileged male-stereotypedcharacteristics. He argued that the use of personalitytests led to a feedback cycle in which companieshired “antisocial, mathematically inclined males,”perpetuating the belief that programmers should beantisocial, mathematically inclined males (p. 78).Ensmenger (2010a) also points to efforts tobring prestige and structure to the field of computing,which involved the creation of professionalorganizations, networks, and hierarchiesthat encouraged and facilitated the entry of men.Abbate (2012) argues that this effort to professionalizethe field further connected computing with

16SOLVING THE EQUATIONengineering, specifically engineering’s technical andanalytical focus and its prestige as a professionalfield. On university campuses an increasing proportionof computer science programs became locatedwithin engineering colleges (Misa, 2010). Thepercentage of women earning bachelor’s degreesin computer science has been shown to be lower atschools where computing programs are located inengineering colleges compared with schools thatlocate their computing departments in colleges ofarts and sciences (Camp, 1997).But what happened specifically in the 1980sthat caused women’s representation in computingto drop? One argument points to the rise ofthe personal computer. IBM launched the personalcomputer in 1981, and Apple introducedthe Macintosh in 1984. Before that, few people’shomes or businesses had computers, and girls andboys had similar exposure to computers—generallynone. Once computers were in the home, theywere rapidly adopted by men and boys as a newkind of toy (Margolis & Fisher, 2002). Haddon(1992), who studied the origins of the home computermarket in England, argued that the personalcomputer linked the computing industry to theelectronic “hobbyist” culture and earlier game-playingarenas, such as arcades, that were primarily thedomain of men and boys. The gamer culture thathas since developed can be particularly inaccessiblefor women (Parkin, 2014).Some have suggested that women beganearning a declining proportion of computer sciencebachelor’s degrees in the 1980s because ofa surge in interest in computing as an academicdiscipline. This boom overloaded the capacityof academic computer science departments,and schools responded by imposing increasinglystringent requirements for entry and completion ofthe major. These requirements disproportionatelydisadvantaged women and people of other underrepresentedgroups who were generally enteringcollege with less programming experience and lessmath preparation (Roberts, 1999, 2011).As in engineering, the proportion of womenacross computing disciplines varies significantly,with women making up about 39 percent of webdevelopers but only 7 percent of computer networkarchitects. In no engineering or computing occupationdoes women’s representation match that in theoverall full-time labor force (44 percent), and comparedwith professional occupations overall, wherewomen make up 57 percent of the workforce (U.S.Department of Labor, Bureau of Labor Statistics,2014a, table 9), women are dramatically underrepresentedin computing (and engineering) jobs.Women are best represented among web developers(39 percent), computer systems analysts (36 percent),and database administrators (32 percent). Inall other computing (and all engineering) occupations,women account for less than 30 percent ofworkers, far below parity (see figure 4).Preparing K–12 studentsfor engineering andcomputingMath and science courses in elementary, middle,and high school can prepare students for pursuingengineering or computing majors in collegeand, especially for engineering, are often required.Mathematics can also be helpful for those whoenter these fields with an associate degree orcertificate or by another means. In elementaryand middle school, girls and boys tend to earnsimilar grades in math and science courses andto have similar scores on standardized math andscience exams (Snyder & Dillow, 2013). In highschool, girls and boys have earned approximatelythe same number of math and science credits, andgirls have been doing slightly better than boys inthese classes, since the 1990s (U.S. Departmentof Education, National Center for EducationStatistics, 2011). Boys are more likely, however, totake the advanced placement exams most closelyassociated with engineering and computing andtend to outscore girls on these exams and the SATby a small margin (College Board, 2013a, 2013b).How relevant are these small gender differencesin early science and math achievement to women’sunderrepresentation in engineering or computing?A recent study by researchers at the University ofTexas and the University of Minnesota found thatgender differences in achievement in high school,including women’s underrepresentation among

AAUW 17those earning the highest standardized math testscores, accounted for very little of the gender differencein the likelihood of choosing an engineering,computing, math, or physical science major incollege (Riegle-Crumb et al., 2012). The researchersdetermined that while some gender differences inSTEM achievement existed before college, thesedifferences did not appear to be a strong determinantof who ended up majoring in engineering andcomputing.Rather than achievement, interest in STEMfields in high school is more closely associated withthe pursuit of an engineering or computing educationor career in adulthood (Maltese & Tai, 2011;Benbow, 2012). While achievement in an areaoften contributes to interest, beginning in youngadolescence and increasingly as girls and boys movethrough middle school and high school, boys tendto express more positive attitudes toward and interestin STEM subjects than girls do (Sandrin &Borror, 2013; Iskander, Gore et al., 2013; Diekman,Weisgram et al., 2015; Else-Quest et al., 2013;Weisgram & Bigler, 2006; Weisgram & Diekman,2014).One nationally representative study found thatthe key factor predicting STEM career interestat the end of high school was interest at the startof high school (Sadler et al., 2012). While it iscertainly possible to decide to pursue a career inengineering or computing well after high schoolgraduation, these findings suggest that earlyexposure to engineering and computing that sparksan interest in these fields is often a precursor toactually pursuing a career in these fields. For thisreason, advocates for improving gender diversity inengineering and computing often focus interventionson increasing interest in the fields amongelementary and middle school students (Valla &Williams, 2012). One recent survey found thatsocial encouragement from family, friends, andeducators, regardless of their technical expertise, isthe factor most likely to encourage girls’ interest incomputer science (Google, 2014b).Lack of computing education in K–12 schoolsJust 19 percent of high school graduates reported earningat least one credit in a computing course during highschool in 2009, and fewer girls (14 percent) than boys (24percent) reported taking such courses. These percentagesare smaller than in both 1990 and 2000, when 25percent of high school graduates reported taking computingclasses (U.S. Department of Education, NationalCenter for Education Statistics, 2011). According to theNational Center for Women and Information Technology(2014a), most students in the United States are notexposed to a rigorous computing education in highschool or earlier for several reasons:1. Only about half of the states allow computing classesto count as an academic subject toward high schoolgraduation (Code.org, 2014). Many states that doallow computing classes to count have only recentlyadopted this policy. AAUW and other organizationssuch as the Computer Science Teachers Associationand Code.org have worked to encourage states tocount high school computing credits as part of themath or science credits required for graduation.2. Computing courses are sometimes included in vocationalcourse choices and, therefore, may not attractcollege-bound students.3. Computing as an elective course competes withother attractive electives, such as music and foreignlanguages.4. Computing is often taught by individuals with eitherlittle background in the subject or little teachingexperience.Computing classes for middle and high school studentsintroduce girls and boys to the subject at an influentialage. These courses are especially useful for sparkinggirls’ interest in computing because girls are less likelythan boys to pursue computing on their own outside of aformal learning environment (Margolis & Fisher, 2002).

18SOLVING THE EQUATIONFIGURE 5. INTENT OF FIRST-YEAR COLLEGE STUDENTS TO MAJOR IN STEM FIELDS, BY GENDER, 2014252019%WomenMenPercentage of students151016%11%6%6%50Biological andlife sciencesEngineering1%Chemistry1% 1%Computerscience1% 1%Mathematics/statistics0.3%Physics1%Source: AAUW analysis of Eagan et al. (2014).Higher educationA gender gap in students’ intention to pursuebachelor’s degrees in engineering or computingis evident by the time they step foot on collegecampuses. 7 Young men are much more likely thantheir female peers to begin college intending tomajor in engineering and computing (see figure 5).While nearly one out of every five young men whostart college intends to major in engineering, onlyone out of 17 young women starting college hasthe same intention. Computing is a much smalleracademic field, and 6 percent of young men whostart college intend to major in computing comparedwith just 1 percent of young women. Thisdivide by gender holds true across races/ethnicities:Women are far less likely than men within eachracial/ethnic category to begin college intendingto major in engineering or computing (NationalScience Foundation, National Center for Scienceand Engineering Statistics, 2014c).Of course, not all students who enroll in anengineering or computing major graduate withthose degrees. At the bachelor’s degree level, thenational rate of retention from entry into the majorto graduation is approximately 60 percent forengineering and 40 percent for computing (Chen,X., 2013), and some evidence shows that this rate issimilar for women and men in engineering (Lord etal., 2013; Ohland et al., 2008). Still, because womenmake up such a small portion of those who initiallymajor in engineering and computing, understandingwhy women leave these majors is important.As figure 6 shows, women’s representationamong engineering bachelor’s degree recipients hasincreased in a fairly linear fashion, growing from amere 1 percent in 1970 to 21 percent in 2000. After2000, participation dipped slightly, to 19 percentin 2013. Women’s participation in undergraduatecomputer science, in contrast, has been decliningfor decades. Before 1970, women earned between10 and 15 percent of the fewer than 1,000 bachelor’sdegrees awarded in computer science eachyear. Throughout the 1980s it appeared that womenmight reach parity in computing, with women

AAUW 19earning 37 percent of computing bachelor’s degreesin 1984 and 1985. After 1985, however, women’sparticipation in computing reversed course, so thatby 2013 the proportion of computing bachelor’sdegrees awarded to women was half what it hadbeen nearly three decades earlier. 8Women’s representation among engineering andcomputing graduates is much smaller than amongbachelor’s degree recipients overall, where womenreached parity with men in 1985 and earned 57percent of the degrees awarded in 2013. Likewise,in the combined total number of science and engineeringbachelor’s degrees awarded, including thesocial sciences and psychology, women reached paritywith men in 2000 and continued to earn half ofthose degrees awarded through 2013 (see figure 6).Among associate degree earners, women’srepresentation in engineering has hovered around14 percent since 1990. In computing, however,the representation of women has been declining.Women earned half of the associate degreesawarded in computing in 1990, but by 2013 thatnumber had dropped to 21 percent (see figure A4in the appendix).Variation in women’srepresentationThe proportion of women earning engineeringbachelor’s degrees varies substantially among engineeringdisciplines (see figure 7). Mechanical, civil,electrical, and chemical engineering are the largestengineering fields, accounting for nearly two-thirdsof engineering degrees awarded each year. Womenaccounted for 32 percent of chemical engineeringgraduates, 21 percent of civil engineering graduates,and only 12 percent of electrical and mechanicalengineering graduates in 2013. Because mechanicaland electrical engineering are such large engineeringdisciplines, women’s dramatic underrepresentationin these fields is a big factor in women’s underrepresentationin engineering overall. Women fromunderrepresented minority (URM) groups, definedhere as engineers who identify as black, AmericanIndian, Alaska Native, or Hispanic, are also severelyunderrepresented in these disciplines, making upjust 2 percent of those awarded bachelor’s degreesin mechanical and electrical engineering in 2013(AAUW analysis of National Science Foundation,FIGURE 6. BACHELOR’S DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1970–2013Percentage605040302010057%57%58%54%55%57%49%51%47%50%50% 50% 50%43%45%43%37%39%33%30% 37%30%28%29%28%22%21%19%18% 19%17%15%15%13%20%10%18% 18%1%2%1970 1975 1980 1985 1990 1995 2000 2005 2010 2013Engineering Computing All science and engineering AllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; mathematics and computer science; physical sciences;psychology; social sciences; and engineering.Source: L. M. Frehill analysis of data from National Science Foundation, Division of Science Resources Statistics (2013), and National Science Foundation, National Center forScience and Engineering Statistics (2014a).

20SOLVING THE EQUATIONFIGURE 7. BACHELOR’S DEGREES AWARDED IN ENGINEERING, BY DISCIPLINE AND GENDER, 201325,00012%22,208xx%WomenMenPercentageof women20,000Number of degrees awarded15,00010,00021% 12%32%13,509 13,0917,6785, 00039% 10% 28% 23%4,838 4,631 4,505 4,47814%3,49022%2,16830% 45% 14% 31% 25% 20% 15% 8%1,262 1,199 1,130 954 767 735 593 5760MechanicalCivilElectricalChemicalBiomedicalComputer, generalIndustrial and engineeringmanagementAll otherAerospaceGeneralMaterialsEnvironmentalPetroleumAgriculturalArchitecturalSystemsNuclearComputer softwareSource: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014a).National Center for Science and EngineeringStatistics, 2014a, 2014b).The engineering fields with the largest representationof women, on the other hand, tend to bemuch smaller fields (see figure 7). Environmentalengineering, for example, boasts the highest representationof women, who earned 45 percent ofbachelor’s degrees in 2013; yet only about 1,200environmental engineering bachelor’s degrees wereawarded that year. 9 Environmental engineering wasalso the field with the highest percentage of URMwomen, who earned 7 percent of bachelor’s degreesawarded in 2013 (AAUW analysis of NationalScience Foundation, National Center for Scienceand Engineering Statistics, 2014a, 2014b). The fieldof bioengineering/biomedical engineering similarlyhas attracted a relatively high proportion of women(39 percent in 2013) and is somewhat larger, withapproximately 4,800 bachelor’s degrees awarded in2013.

AAUW 21FIGURE 8. BACHELOR’S DEGREES AWARDED IN COMPUTING, BY GENDER AND DISCIPLINE, 201320,00018%18,610WomenMenxx% Percentageof women15,000Number of degrees awarded10,00013%11,13517%6,565 22%5,8295,00034%3,8740Computer and informationsciences, generalComputer scienceComputer/IT administrationand managementInformation science studiesSoftware and mediaapplications10%1,828Systems networking andtelecommunications13%1,692Programming21%1,516Systems analysis18%537Computer/IS support servicesincluding data processingSource: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014a).Percentages aside, the largest numbers ofbachelor’s degrees awarded to women in 2013 werein civil engineering (2,894), mechanical engineering(2,676), and chemical engineering (2,478).Similarly, the largest numbers of bachelor’s degreesearned by URM women were in civil engineering(526), mechanical engineering (415), and chemicalengineering (355). At the other end of the spectrum,only 16 URM women were awarded bachelor’sdegrees in petroleum engineering in 2013,accounting for just 1 percent of the petroleumengineering bachelor’s degrees awarded that year(AAUW analysis of National Science Foundation,National Center for Science and EngineeringStatistics, 2014a, 2014b).The proportion of women earning computingbachelor’s degrees also varies by discipline (seefigure 8). General computer and information sciencesis the largest discipline, making up more thana third of all bachelor’s degrees awarded in 2013.Eighteen percent were awarded to women, and 4percent were awarded to URM women. Womenwere best represented in software and mediaapplications, where they earned about a third of the

22SOLVING THE EQUATIONbachelor’s degrees awarded in 2013. Informationscience and systems analysis are the fields with thenext highest percentage of women, at 22 and 21percent. These fields have the best representationof URM women as well, with URM women mosthighly represented in software and media applications(9 percent) and in information science andsystems analysis (7 percent). At the other end ofthe spectrum, women were least well representedin systems networking and telecommunications,where they were awarded just one in 10 bachelor’sdegrees in 2013. URM women were least well representedin computer science, where they receivedonly 2 percent of bachelor’s degrees in 2013(AAUW analysis of National Science Foundation,National Center for Science and EngineeringStatistics, 2014a, 2014b).Diversity among U.S. engineeringand computing graduatesEngineering and computing have not achieved levelsof gender and racial/ethnic diversity on par withthose of the general population between the ages20 and 24. Women of all races/ethnicities exceptAsian are underrepresented among engineering andcomputing bachelor’s degree recipients comparedwith their representation in the general population.White women were awarded 13 percent of theengineering and 10 percent of the computing bachelor’sdegrees in 2013, while making up 28 percentof the general population. The largest discrepancyis among URM women, who were awarded just3 percent of the engineering and 6 percent of thecomputing bachelor’s degrees in 2013, while makingup 18 percent of the general population (seefigure 9).How men are faringWhen it comes to men, the story is different—atleast in the number of degrees awarded. Men of allracial/ethnic groups are much better representedamong engineering and computing bachelor’sdegree earners than are women of the same group.In most cases, men are earning engineering andcomputing degrees in proportion to or better thantheir representation in the general population (seefigure 9 and figure A5 in the appendix). This istrue for white and Asian American men in bothengineering and computing. Black men, too, whorepresented 8 percent of the 20- to 24-year-olds inthe United States, earned 9 percent of computerscience bachelor’s degrees in 2013. 10 AmericanIndian/Alaska Native men, who represented just0.5 percent of 20- to 24-year-olds in the UnitedStates, earned 0.5 percent of the computer sciencebachelor’s degrees in 2013 (see figure A5 in theappendix).Together, black, Hispanic, and AmericanIndian/Alaska Native men were awarded 18 percentof computing bachelor’s degrees in 2013 andmade up 19 percent of the general population of20- to 24-year-olds. This near parity is especiallynoteworthy given that men from these racial/ethnicgroups are significantly underrepresented amongbachelor’s degree earners overall, making up just 9percent of those earning bachelor’s degrees in anyfield in 2013. Any underrepresentation of black,Hispanic, and American Indian/Alaska Nativemen among engineering and computing bachelor’sdegree holders is largely symptomatic of the issueof insufficient diversity in higher education overall(Su, 2010). In the engineering and computingworkforce, however, black, Hispanic, and AmericanIndian/Alaska Native men are underrepresented.Between 2006 and 2010, black men made up just 4percent of the engineering and computing workforce.Hispanic men made up 5 percent of the engineeringand 4 percent of the computing workforce,and American Indian/Alaska Native men madeup 0.2 percent of the engineering and computingworkforce.Diversity of those with advanced degreesFigure 10 underscores the degree to which graduatesof engineering and computing programs arenot representative of the U.S. population andprovides a look at the breakdown at the master’sand doctoral levels as well. 11 While white womenmade up 30 percent of high school graduates in2012 and URM women made up 18 percent, just7 percent of doctoral degrees in engineering andcomputing were awarded to white women, andjust 2 percent were awarded to URM women.

AAUW 23FIGURE 9. POPULATION AGES 20–24 AND BACHELOR'S DEGREES AWARDED IN SELECTED FIELDS,BY RACE/ETHNICITY AND GENDER, 2013Population, 20–24 Years Old(n = 22,252,501)3%All Bachelor’s Degrees(n = 1,658,333)4%29%18%30%15%19%28%9%3%39%3%Computing Bachelor’s Degrees(n = 44,193)51%Engineering Bachelor’s Degrees(n = 76,246)2%6%3% 3%10%13%8%10%56%59%18%12%URM womenWhite womenAsian American women Asian American menURM menWhite menNotes: Charts include only U.S. citizens and permanent residents. U.S. citizens and permanent residents of “other/unknown races/ethnicities” (which includes students who reportmultiple race/ethnicities) and temporary residents are not included. Computing included 5,276 other and 2,365 temporary residents, and engineering included 4,637 other and6,331 temporary residents who earned bachelor’s degrees and, therefore, were not included in these data. Underrepresented minority (URM) includes American Indians and AlaskaNatives, blacks, and Hispanics/Latinos.Sources: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014b), and U.S Census Bureau (2014d).

24SOLVING THE EQUATIONFIGURE 10. STUDENT POPULATION FROM HIGH SCHOOL TO DOCTORATE IN ENGINEERING ORCOMPUTING, BY RACE/ETHNICITY AND GENDER, 20121008018%30%3%18%31%3%4%11%8%12%2%7%6%6%30%3%3%7%5%24%2%2%3%Percentage604016%3%14%3%55%12%12%46%30%28%33%200High schoolgraduates(n = 3,014,670)1%2%First-time, full-timefreshmen incollege overall(n = 2,268,993)6%Bachelor’s degreein engineeringor computing(n = 121,310)1%Master’s degreein engineeringor computing(n = 59,952)Ph.D. in engineeringor computing(n = 10,084)Asian womenAsian menURM womenURM menWhite women Temporary resident womenWhite men Temporary resident menNotes: Students and degree recipients reported as “other/unknown races/ethnicities” were excluded from the totals and percentages reported here. As a result, the following numbersare excluded: first-time, full-time freshmen, 185,677; bachelor’s degree, 9,913; master’s degree, 4,199; and Ph.D., 479. Underrepresented minority (URM) includes AmericanIndians and Alaska Natives, blacks, and Hispanics/Latinos.Sources: Prescott & Bransberger (2012); L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014b).Part of the reason for the small percentages is thatmore than half (58 percent) of engineering andcomputing doctorates were awarded to temporaryresidents, four of five of whom were men. Lookingonly at U.S. citizens and permanent residents, 16percent of engineering and computing doctorateswere awarded to white women, and 4 percentwere awarded to URM women (AAUW analysisof National Science Foundation, National Centerfor Science and Engineering Statistics, 2014b), stillwell below the representation of white and URMwomen in the overall population. In sum, engineeringand computing largely remain men’s domains atthe college level, and URM women are particularlyunderrepresented (Ong, 2011).People with disabilitiesIncreasing the number of people with disabilitiesin engineering and computing is another part ofbuilding diversity. About 12 percent of the populationhas some kind of disability (National ScienceFoundation, National Center for Science andEngineering Statistics, 2014c). College studentswith disabilities are somewhat more likely tomajor in a STEM field than are students withoutdisabilities (Lee, 2011). Students with autismspectrum disorder are especially likely to select amajor in a STEM field (Wei et al., 2014). Studentswith disabilities are about as likely to graduate inengineering or computing as are students withoutdisabilities; however, people with disabilities are

AAUW 25underrepresented in the engineering and computingworkforce relative to the workforce as a whole.An estimated 6 percent of working computingprofessionals and 7 percent of working engineersreport having a disability, compared with 9 percentof workers overall (National Science Foundation,National Center for Science and EngineeringStatistics, 2014c).Training for the engineeringworkforceMost working engineers (80 percent) have at leasta bachelor’s degree, with just over a quarter holdingan advanced degree (23 percent hold a master’sdegree, and 5 percent hold a doctorate). Mostengineers with less than a bachelor’s degree levelof education have an associate degree (AAUWanalysis of U.S. Census Bureau, 2011b).The percentage of women among engineeringgraduates varies dramatically among institutions.Many different kinds of institutions have succeededin graduating a relatively large proportionof women in their engineering programs. The fewwomen’s colleges that have engineering programs,such as Smith College and Sweet Briar College,graduate 100 percent women. Historically blackcolleges and universities (HBCUs), such as PrairieView A&M University, which awarded 66 percentof its engineering bachelor’s degrees to women in2012, and top-rated schools, such as MassachusettsInstitute of Technology and California Institute ofTechnology, both of which awarded 44 percent oftheir bachelor’s degrees in engineering to women in2012, have also succeeded in graduating engineeringclasses with a higher than average percentage ofwomen. At most of the largest engineering schoolsin the country, women make up about one of everyfive graduates (see figures A6a and A6b in theappendix).Training for the computingworkforceComputing professionals arrive at their occupationsby more varied means than engineers do.Computing jobs are available for workers at all levelsof educational achievement. A small percentage(6 percent) of computing professionals hold only ahigh school diploma or GED (general educationaldevelopment), and 1 percent have not graduatedfrom high school. About a third (29 percent) ofcomputing professionals have some college experience,a certificate (typically requiring one year ofstudy), or an associate degree (typically requiringtwo years of study). 12 Most computing professionals(about two-thirds) have a bachelor’s degree orhigher; 44 percent hold a bachelor’s degree, 18percent hold a master’s degree, and 2 percent hold adoctorate (AAUW analysis of U.S. Census Bureau,2011b).Of computing professionals with bachelor’sdegrees or higher, only about half have bachelor’sdegrees in computing, engineering, or mathematicalsciences. 13 Nearly a fifth of computing professionalswith bachelor’s degrees were businessEducational attainment ofthe engineering andcomputing workforceIn this report AAUW follows the protocol of the U.S.Department of Labor, Bureau of Labor Statistics,and the U.S. Census Bureau in defining “engineers”and “computer occupations.” Both report workforceinformation separately for “engineers” and “drafters,engineering technicians, and mapping technicians,”effectively providing data separately forengineering jobs that require bachelor’s degreesand those that do not.In contrast the category of “computer occupations”combines occupations that require varying levelsof education, including those that require a doctorateand those that require an associate degreeor less (U.S. Department of Labor, Bureau of LaborStatistics, 2009). So, for example, while AAUW doesnot include engineering technicians and drafters inthe definition of engineers, computer support personnelare included in the definition of computingworkers. The result is that the educational backgroundsof the engineers and computing professionalsthat are the subject of this report are somewhatdifferent.

26SOLVING THE EQUATIONmajors, and others majored in the social sciences,the physical or biological sciences, communications,and psychology (AAUW analysis of U.S. CensusBureau, 2014a). 14The approximately one-third of computer workerswho hold technical bachelor’s degrees attendedmany different kinds of institutions, includingpublic, private nonprofit, and private for-profitinstitutions. The University of Phoenix, a for-profitinstitution, was far and away the largest institutionin the United States to grant bachelor’s degrees incomputing in 2012, awarding just over 3,000. As inengineering, the percentage of computing degreesawarded to women ranges widely from less than10 percent at some institutions to two-thirds ofdegrees at other institutions, and many differentkinds of institutions have succeeded in graduatinga large proportion of women in computingprograms. For-profit schools, including several ArtInstitute campuses; HBCUs, such as Johnson C.Smith University; 15 and public and private institutions,such as Northwest Missouri State University,North Carolina Wesleyan College, and HarveyMudd College, are among the top institutions inwomen’s representation among computing graduates(see figures A6c and A6d in the appendix).The engineering andcomputing workforceThe great demand for workers in the engineeringand computing fields is an important reasonto attract more women (Sevo, 2009). Overall,employment in the United States is projected togrow by approximately 15 million jobs (11 percent)between 2012 and 2022. The U.S. Department ofLabor, Bureau of Labor Statistics (2014d), predictsthat computing occupations will grow at asubstantially faster rate of 18 percent. Some of thefastest-growing computing occupations are amongthose expected to produce the most growth in rawnumbers of jobs as well, resulting in 1.2 millioncomputing job openings between 2012 and 2022.Computing occupations that are predicted to growespecially quickly (see figure A7 in the appendix)include the relatively small field of informationsecurity analysts (projected to grow 37 percent by2022); computer systems analysts (25 percent);and the largest computing occupation and largestSTEM occupation overall (Landivar, 2013),applications software developers (23 percent). Allof these occupations require a bachelor’s degree foran entry-level position (U.S. Department of Labor,Bureau of Labor Statistics, 2014e), and most workersin these occupations have a bachelor’s degree orhigher in computing or engineering fields (AAUWanalysis of National Science Board, 2014, appendixtable 3-3).Engineering occupations, on the other hand,are predicted to grow at a slower rate of 9 percent,resulting in slightly more than 500,000 engineeringjob openings between 2012 and 2022. Theengineering occupations predicted to grow at thefastest rates (see figure A8 in the appendix) includethe relatively small field of biomedical engineering(predicted to grow by 27 percent by 2022), thesimilarly small field of petroleum engineering (26percent), and one of the largest engineering fields,civil engineering (20 percent).Retention in engineeringIn addition to being less likely than men to gointo engineering in the first place, women appearto be less likely than men to stay in the engineeringprofession (Hunt, 2010; Frehill, 2012). Asshown in figure 11, AAUW’s analysis of NationalScience Foundation data finds that while most(about 65 percent) women and men who graduatewith bachelor’s degrees in engineering initially takeengineering jobs, by 10 years into their careers, onlyabout 40 percent of graduates, both women andmen, remain in engineering. While men’s retentionrate levels off at around 40 percent for the next 25years, the retention rate for women continues todecline. Thirty years into their careers (by the timewomen and men are in their 50s), women are halfas likely as men to report that they are still workingas engineers. 16Retention in computingAs in engineering, women appear to be morelikely than men to leave the computing workforce(Hewlett, Buck Luce et al., 2008). Among

AAUW 27FIGURE 11. RETENTION IN ENGINEERING, BY GENDER, 2010706065%64%WomenMenPercentage of engineering graduatesworking in engineering504030201046%43%40%35%37%34%35%30%36%23%39%19%00–45–910–1415–1920–2425–2930–34Years since bachelor’s degree completedNotes: Includes only individuals who reported a bachelor’s degree in engineering and no additional educational credential as of 2010. Includes women and men who reportedearning a bachelor’s degree in engineering as well as working in an engineering occupation in either the National Survey of College Graduates or the National Survey of RecentCollege Graduates administered in October 2010.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2010a, 2010b).FIGURE 12. RETENTION IN COMPUTING, BY GENDER, 2010Percentage of computing graduatesworking in computing occupations7060504030201057%28%59%45%56%37%52%46%45%43%52%32%WomenMen36%32%00–45–910–1415–1920–24Years since bachelor’s degree completed25–2930–34Notes: The public data used for this analysis combined computing and mathematics occupations and degrees; however, mathematics is typically small compared to computing inboth employment and educational data. Includes individuals who reported earning a bachelor’s degree in computing or mathematics and no additional educational credential andwho were working in a computing or mathematical occupation in either the National Survey of College Graduates or the National Survey of Recent College Graduatesadministered in October 2010.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2010a, 2010b).

28SOLVING THE EQUATIONPatentingDescribing women’s participation in engineering andcomputing overall is important, but it is also importantto measure how women are participating. Level of participationis more difficult to measure than simple representation,but patenting is one metric of innovationand influence in engineering and computing.A patent is a set of exclusive rights granted by a governmentto an inventor to manufacture, use, or sell aninvention for a certain number of years. A recent analysisof patenting rates found that only 5.5 percent ofcommercialized patents are held by women. This disparitywas found to be due in large part to women’sunderrepresentation in engineering, specifically electricaland mechanical engineering, which are the mostpatent-intensive STEM fields and fields in which womenare particularly underrepresented (Hunt et al., 2012).A recent analysis of patenting in computing found thatbetween 1980 and 2010, only 13 percent of U.S.-inventedcomputing patents had at least one female inventor.Although overall patenting rates for women in computinghave been and remain quite low, the trend ispositive, with more women being awarded patents forcomputing inventions in recent years compared withthe past. Importantly, this increase happened during aperiod in which women’s participation in computing wasdeclining, which makes the increase particularly noteworthy(National Center for Women and InformationTechnology, 2012).Nonetheless, while the trend is positive, the large gendergap in patenting suggests that women are significantlyless well represented among those doing leading-edgework in engineering and computing than inthese fields overall (Rosser, 2012).FIGURE 13. FEMALE FACULTY, BY RANK AND DISCIPLINE, 2004 AND 2013Percentage of faculty who are women3025201510516%26%12%20%10%13%18%23%12%17%6%200420139%0AssistantAssociateFullAssistantAssociateFullCOMPUTING PROFESSORSENGINEERING PROFESSORSSource: L. M. Frehill analysis of data from American Society for Engineering Education (2005, 2014) and Computing Research Association (2005, 2014).

AAUW 29all workers with bachelor’s degrees in computingin 2010, 54 percent of men were working in thecomputing field, whereas only 43 percent of womenwere (National Science Board, 2014, appendixtable 3-15). Unlike engineering, however, a genderdifference in participation appears to start immediatelyafter college graduation. AAUW’s analysis ofNational Science Foundation data (2010a, 2010b)finds that in the first few years after earning computingdegrees, 28 percent of women and 57 percentof men report working in a computing job (seefigure 12). Men with bachelor’s degrees in computingare less and less likely to work in a computingoccupation the farther away from graduation theyget, while women who graduated longer ago aresometimes more likely to be in a computing ormath occupation than women who graduated morerecently. The trends shown in figure 12 suggest noeasy explanation for computing retention rates formen or women. The volatility of the computinglabor market and the rapid pace of technologicalchange may be important in understanding thecareer paths of both women and men with bachelor’sdegrees in computing.Glass and colleagues (2013) found that womenworking in engineering and computing are morelikely to leave their occupational fields than arewomen in other fields. After about 12 years,50 percent of women who originally worked inSTEM, predominantly engineering and computing,had exited and were employed in other fields.In contrast, only about 20 percent of womenprofessionals in other fields, such as management,financial operations, and nursing, exited their professionaloccupation throughout the course of thestudy, which spanned almost 30 years. The disparityin retention between STEM and non-STEMprofessionals was found to be almost entirely due toSTEM women switching out of STEM fields butnot out of the labor force.The academic workforceAs educators of the next generation of engineersand computing professionals, faculty at U.S. collegesand universities represent a critical part ofthe engineering and computing workforce. Collegeprofessors are role models, influencing who entersthe field and who succeeds in it. Women andpeople of other underrepresented groups are scarceon engineering and computing faculties at U.S.colleges and universities, with white and Asianmen making up the large majority of faculty at alllevels and most dominant among full professors(see figures A9 and A10 in the appendix). Womenare better represented at the entry level of assistantprofessor (23 percent in engineering and 26 percentin computing) than at the typically tenured associatelevel (17 percent in engineering and 20 percentin computing) and the more-senior full level (9percent in engineering and 13 percent in computing).(Some of these numbers do not match whatMethodologyThis report is based on a review of academic literature,expert advice from an advisory panel,and interviews with leading researchers. The literaturereview spanned numerous fields, includingpsychology, computing, organizational behavior,management, sociology, economics, and engineeringeducation, among others. Using multiple databases,including ProQuest Summon, Web of Science,Psycinfo, and JSTOR, AAUW reviewed more than 750publications, most written within the past 15 years,related to the topic of women in engineering andcomputing occupations and focusing on empiricalresearch with practical applications.AAUW identified key, recurrent themes in the literatureand, along with a team of expert advisersdrawn from academia, industry, associations,and government, selected research findings thathave been published or accepted for publicationin a peer-reviewed research venue within the last15 years to feature in the report. These findings,described in chapters 3 through 9, shed light onfactors contributing to the underrepresentation ofwomen in engineering and computing. In additionthe findings have the potential to increase publicunderstanding and influence policies and practicesrelated to this issue.

30SOLVING THE EQUATIONis shown in figures A9 and A10 exactly because ofrounding corrections.) This trend holds true withinracial/ethnic groups as well, with a substantial portionof the few URM women who obtain entrylevelfaculty positions in engineering and computingnot advancing through the ranks (Hess, C., etal., 2013). Black, Hispanic, and American Indian/Alaska Native (URM) women together make upjust 1 percent of full professors in engineering and0.4 percent of full professors in computing (seefigures A9 and A10).The representation of women on engineeringand computing faculties has increased over time(see figure 13). Among full professors, for example,women’s representation grew from 10 percent to13 percent in computing and from 6 percent to 9percent in engineering from 2004 to 2013. At theentry level of assistant professor, the representationof women among college and university faculty hasincreased in computing (from 16 percent in 2004to 26 percent in 2013) and in engineering (from 18percent in 2004 to 23 percent in 2013) during thepast decade. 17 The percentages remain low, however,and many engineering and computing studentslikely still never have a female professor.SummaryGirls are graduating from high school on nearlyequal footing with boys in terms of math andscience achievement. Yet young women—acrossracial/ethnic lines—pursue engineering andcomputing in much smaller numbers than youngmen do. By college graduation, women are greatlyoutnumbered by men in every engineering andcomputing discipline. Likewise, in the workforcewomen are dramatically underrepresented in thefields of engineering and computing and are morelikely to leave these fields than their male counterpartsare. At the faculty level, women remainunderrepresented, especially in more-senior positions.The next chapter explores the reasons behindthese gender differences.Chapter 1 notes1. In this report STEM refers to the physical, biological, and agricultural sciences; computer and information sciences; engineeringand engineering technologies; and mathematics, unless otherwise noted. The social and behavioral sciences, such as psychologyand economics, are not included, nor are health workers, such as doctors and nurses.2. AAUW’s analysis of U.S. Department of Labor, Bureau of Labor Statistics (2014e) data finds that “computer and informationresearch scientist” is the only computing or engineering occupation of 31 such occupations to require education beyond abachelor’s degree for an entry-level position. Computer and information research scientists make up less than 1 percent of theengineering and computing workforce.3. AAUW’s analysis of U.S. Department of Labor, Bureau of Labor Statistics (2014e) data finds that nine of 27 science and mathoccupational categories require education higher than a bachelor’s degree for an entry-level position. These categories includeanimal scientists, physicists, astronomers, biochemists and biophysicists, medical scientists, hydrologists, epidemiologists, mathematicians,and statisticians. Professionals in these occupations make up approximately 28 percent of the scientific and mathematicalworkforce.4. Although the U.S. Census Bureau’s 2010 American Community Survey reported that 4 percent of workers (and 6 percent ofcomputer, engineering, and science workers) “usually” worked from home in 2010, the Census Bureau’s 2010 Survey of Income andProgram Participation (SIPP) reported that 10 percent of workers worked from home “at least one full day a week” in a typicalmonth. Both sources find that more and more workers are working from home over time (Mateyka et al., 2012).

5. In this report, AAUW defines engineering professions to include those that fall under the 17-2000 “engineers” category of the2010 Standard Occupational Classification (U.S. Department of Labor, Bureau of Labor Statistics, 2009). Engineering techniciansand drafters are not included in this definition. See figure A1 for the full list.6. In this report, AAUW defines computing occupations to include those that fall under the 15-1100 “computer occupations”category of 2010 Standard Occupational Classification (U.S. Department of Labor, Bureau of Labor Statistics, 2009). See figure A2for the full list.7. Throughout this report, AAUW defines academic computing disciplines as those included in the 2010 Classification ofInstructional Programs (U.S. Department of Education, National Center for Education Statistics, 2010) “computer and informationsciences and support services” category (CIP code 11), with the exception of CIP code 11.06, data entry, which the NationalScience Foundation does not include in the “detailed” field of computer science. Likewise, the academic engineering discipline isdefined to include the fields included in CIP code 14, with the exception of CIP code 14.3701, “operations research,” which NSFincludes in the field of business and management. Engineering also includes three additional disciplines that NSF includes inits category of engineering: engineering/industrial management (CIP code 15.1501), geographic information science (CIP code45.0702), and materials science (CIP code 40.1001). For a list of academic disciplines included in engineering and computing inthis report, see figures A11 and A12 in the appendix.8. The number of bachelor’s degrees in computer science awarded to women has dropped not only in percentages but in actualdegrees awarded as well. In 1986 slightly more than 15,000 women earned bachelor’s degrees in computer science (NationalScience Foundation, Division of Science Resource Statistics, 2013), but by 2013 that number had dropped to approximately9,000 bachelor’s degrees (National Science Foundation, National Center for Science and Engineering Statistics, 2014a). On theother hand, men earned more bachelor’s degrees (slightly more than 42,000) in computing in 2013 than in past decades. The onlyyear in which men earned more computing bachelor’s degrees than 2013 was 2004, when nearly 45,000 men earned computingdegrees (National Science Foundation, Division of Science Resources Statistics, 2013).9. These numbers may underestimate the total number of environmental engineering degrees awarded because some civil andchemical engineering programs also may have an environmental focus. The environmental engineering bar shown in figure 7includes only those 2013 bachelor’s degrees reported to the U.S. Department of Education, National Center for EducationStatistics awarded in the primary field of environmental/environmental health engineering.10. Although engineering is a much bigger field than computing, more black women and men earned degrees in computer sciencethan in engineering in 2013. The reverse, and more expected, scenario is true for Hispanic/Latinos and American Indians/AlaskaNatives, who were awarded more degrees in engineering than in computer science (see figure A5 in the appendix).11. Figures A13 and A14 in the appendix show the percentage of master’s and doctoral degrees awarded to women from 1970 to2013 in all fields, all science and engineering fields, engineering, and computing.12. Computer user support specialists and web developers are two fast-growing computing occupations that require an associatedegree or less for an entry-level position (U.S. Department of Labor, Bureau of Labor Statistics, 2014e).13. Computing occupations with large numbers of workers who hold bachelor’s degrees or higher in computing majors include computersoftware engineers and applications and systems software developers (AAUW analysis of National Science Board, 2014,appendix table 3-3).14. Computing occupations with large numbers of workers who hold bachelor’s degrees but not computing or engineering degreesare web developers and database administrators (AAUW analysis of National Science Board, 2014, appendix table 3-3).15. Minority-serving institutions, including historically black colleges and universities, Hispanic-serving institutions, and tribalcolleges and universities, have a strong history of graduating relatively high percentages of female STEM majors, including computingmajors, who go on to earn doctorates. The persistence of women in computing in these institutions has been attributed, inpart, to nurturing environments, faculty who believe in their students, and a collaborative peer culture (Ong, 2011).16. Gender differences in retention in the engineering workforce may vary based on engineering discipline. Some evidence suggeststhat attrition rates from the workforce are similar for women and men in civil engineering but that women are less likely thanmen to stay in mechanical and electrical/computer engineering fields (Frehill, 2010).17. The faculty data shown in figure 13 and in figures A9 and A10 in the appendix have an important caveat: They represent dataonly from the institutions that replied to the 2013 American Society for Engineering Education (2005, 2014) and Taulbeesurveys (Computing Research Association, 2005, 2014). The ASEE numbers include data from institutions that award approximately95 percent of engineering bachelor’s degrees, so they are a good representation of the field of engineering professors. Forcomputing, the caveat is more important. CRA data include only institutions that confer doctoral degrees, meaning that figures13 and A10 show data for the faculty at 143 institutions that together award only about one-fourth of computing bachelor’sdegrees, since many computer science degrees are awarded by institutions that do not offer doctoral degrees, including many forprofitinstitutions.AAUW 31

Chapter 2.Why So Few?

34SOLVING THE EQUATIONStudy after study finds that women have ability,good grades, and high test scores in STEMsubjects, and yet women are turning away, or beingpushed away, from engineering and computingfields. A theme that overarches much of theresearch on this topic is that women often feel asif they don’t fit or belong in these fields. Researchinto this perceived lack of fit provides a complexpicture of social and environmental factors influencingand interacting with individual motivationsand values that are, in turn, also influencedby the wider culture. This chapter describes thelatest research on structural and cultural factors inengineering and computing that may contribute towomen’s underrepresentation in these fields.Structural and culturalbarriersAs past decades have shown, simply trying torecruit girls and women into existing engineeringand computing programs and workplaces hashad limited success. Catalyst (2014) found thatwomen in business roles at technical companies,like women in technical roles at these companies,tend to leave at higher rates than their male peersdo (53 percent of women compared with 31 percentof men after their first post-MBA job). Thisfinding suggests that the overall workplace cultureand environment in technical industries may notbe working for women, whether or not they arein technical roles. In both college and workplaceenvironments, institutional structures and practicesand more general cultural factors may contribute tothe underrepresentation of women in engineeringand computing fields.Narrow focusOne significant impediment, according to somescholars, is an emphasis on logical thinking at theexpense of critical thinking in engineering culture(Claris & Riley, 2012). Scholars have pointed to aculture in engineering that discourages thinkingbeyond the technical parameters of a given problem(Cech, 2014). Engineering students, for example,are rarely asked to reflect on what they do, why theydo it, and what the implications might be (Baillie& Levine, 2013). Specifically, the “culture of disengagement”in many college engineering programsdoes a poor job of training future engineers in theirethical and social responsibilities and cultivates anunderstanding of nontechnical concerns, such asthe public welfare, as irrelevant to “real” engineeringwork (Cech, 2014). These elements of engineeringculture are not confined to the college environmentbut persist once engineers enter the workforce,and although they are likely to discourage manywomen and men from pursuing engineering, theyare perhaps especially discouraging for womenbecause women are more likely than men to expressa preference for work with a clear social purpose(Konrad et al., 2000).Engineering and computing programs typicallyinclude high course load and workload requirements.Engineering curricula, in particular, areoften rigid, making it difficult for students totransfer into engineering if they do not start outin it. One study attests to this difficulty, findingthat 90 percent of those studying engineering intheir eighth semester in college had identifiedengineering as their major when they began college(Ohland et al., 2008). Among students with strongmathematical aptitude (including most engineeringand computing students), women are morelikely than men to also have strong verbal aptitude(Wang et al., 2013). The constrained curriculumin engineering and computing may make it difficultfor students to take elective courses in otherfields or take advantage of other extracurricularopportunities that can be valuable contributors tothe college experience, especially for students withbroad interests and aptitudes. For example, onestudy found that students majoring in engineeringand computing were the least likely of all studentsto take foreign language courses or participate instudy abroad programs (Lichtenstein et al., 2010).IsolationWomen in engineering and computing fields oftenreport isolation, a lack of voice, and a lack of support(Ayre et al., 2013; Fouad et al., 2012; Hewlett,Buck Luce et al., 2008; Hewlett, Sherbin et al.,2014; Servon & Visser, 2011). A study of womenand men working in technology at 21 high-tech

AAUW 35companies found that women were less likely thanmen to indicate that their supervisors were receptiveto suggestions, less likely to say that theirsupervisors were available when they needed them,and less likely to agree that “it is safe to speak upmost of the time” (Catalyst, 2008). In one studyof women in private-sector technical jobs, a thirdsaid that they felt extremely isolated at work. Inthe same study, four of 10 female engineers andcomputing professionals reported lacking rolemodels, while about half reported lacking mentors(Hewlett, Buck Luce et al., 2008).Stereotypical surroundingsThe physical environment in engineering orcomputing classrooms and workplaces can makea difference in how comfortable women find theenvironment. In one study, female students whoentered a room containing stereotypical “geek”objects were less likely to identify themselves withcomputing or feel they belonged with a companyor on a team (even an all-female team) than didwomen who entered a room containing genderneutralobjects (Cheryan et al., 2009).Social networks less helpfulfor womenSocial networks appear to be less beneficial forwomen than for men, perhaps especially in computing.An analysis of social networks amongundergraduate management information systems(MIS) students as they searched for jobs foundthat although social networks improved individuals’job prospects, women’s social networks did notprovide the job opportunities that men’s networksdid (Koput & Gutek, 2010). For example, one malestudent who had a C average, many male contacts,and very few female contacts participated in 16job interviews, received five job offers, and startedhis career with a high salary. In contrast, womenin the study, who also had many male contacts,generally did not get many job interviews or endup with high salaries, even if they had high grades.According to Koput and Gutek (2010, p. 71),“Women high in aspects of human capital, socialcapital, or both did not fare as well as might havebeen anticipated or as well as seemingly similarmen.” The same study found that women withstrong ties to men were more likely than otherwomen to be seen as legitimate in the technical,male-dominated MIS field. Other research hasidentified social networks of powerful men, fromwhich women are excluded, as barriers for womenin engineering workplaces (Faulkner, 2009a).Work-life balance issuesWork-life balance is an important issue for workers,especially women, in engineering and computing.Some researchers argue that rather than work-lifebalance, the real issue is a “culture of overwork.”Organizational cultures of overwork result in dissatisfactionamong women and men (Padavic &Ely, 2013). Because culturally women are expectedto fulfill the responsibilities associated with homeand family and men are expected to be the breadwinners,women may experience negative outcomesas a result of this culture of overwork more frequentlythan men do. For example, a survey of midlevelscientists and engineers in high-tech companiesfound that women were more likely than mento suffer poor health and to delay or forgo gettingmarried and having children as a result of workdemands (Simard et al., 2008). When employersin male-dominated fields such as engineeringand technology expect employees to work longhours (more than 50 hours per week), women withchildren are much more likely than men or childlesswomen not only to leave their employer but toexit the paid workforce entirely (Cha, 2013). Thisresearch suggests that when work responsibilitiesbecome incompatible with the demands of familylife, women, especially mothers, find themselvesin a situation in which they must choose betweenwork and family.Relatively little research has explored whywomen leave engineering and computing fields.One study found that half of women who leftcorporate science, engineering, and technologyjobs moved to technical jobs outside the corporatesector, and the rest moved to jobs outside STEMfields altogether (Hewlett, Buck Luce et al., 2008).Preston (2004) identified a lack of mentoring, amismatch of interests, and difficulty balancing work

36SOLVING THE EQUATIONand family responsibilities as reasons for leaving.Frehill (2012) found that women were more likelythan men to cite a “change in career or professionalinterests” as the most important reason they leftengineering. The first study to comprehensivelyinvestigate factors related to women’s decisions toleave or stay in engineering careers (Fouad et al.,2012) is described in chapter 9. It identifies factorssuch as work environment and access to trainingand development as key to women’s decisions tostay in or leave their engineering jobs.Challenging academic workplacesWomen working in academic engineering andcomputing jobs face challenges similar to thoseof other women working in engineering andcomputing. Women in academic STEM environmentsreport lower job satisfaction than their malecounterparts do (National Research Council, 2010;Bilimoria et al., 2008), although some evidencesuggests that this gender difference in satisfactionhas disappeared among engineering and computingfaculty in recent years (Ceci et al., 2014).Personal experiences with sexual harassment orgender discrimination are the most likely factorsto affect job satisfaction (Settles, Cortina, Malley,et al., 2006), but studies have also connected thegeneral workplace climate—including perceptionsof more work-family interference, less support,gender mistreatment, and an overall impression ofthe workplace as more competitive and hostile—tolower job satisfaction (Settles, Cortina, Malley etal., 2006; Marchetti et al., 2012). In one study of765 STEM faculty members, women ranked theirworkplace environment more negatively than mendid in six of eight measures, considering it moreformal, less exciting, less helpful, less creative, morestressful, and less inclusive. Women in academicscience and engineering also reported fewer conversationswith colleagues about research, loweraccess to human and material resources, and lowerrecognition of accomplishments (Fox, 2010).Some researchers have suggested that genderdiscrimination in academia is a thing of the pastand that the remaining obstacles to women’s fullparticipation in academic STEM fields are workfamilybalance and pre-college decisions that resultin fewer women majoring in fields such as engineeringand computing (Ceci et al., 2014). Indeed,as described in chapter 1, women are less likelythan men to start college intending to major inengineering or computing, and efforts to encouragegirls’ interest in STEM subjects may prove usefulin increasing the representation of women in thesefields. Likewise, some telling statistics point tothe difficulties that mothers still face in academicenvironments. Mason and Goulden (2002) foundthat among science professors who became parentswithin the first five years after receiving a doctorate,77 percent of the men but only 53 percent ofthe women had achieved tenure 12 to 14 years afterearning a doctorate. These numbers support thecontention that work-family balance is an obstacleto women’s full participation in academic STEMworkplaces. Rather than showing that gender discriminationno longer exists in academia, however,these numbers may point to environments withpolicies and structures that make it difficult forwomen with children to thrive.While research has found that women whoapply for tenure-track positions in math-intensivefields are as likely as their male peers to receiveoffers, qualified women are less likely than theirmale peers to apply for these positions (NationalResearch Council, 2010), perhaps because theyperceive academic settings as environments thatwill not support them in achieving their life goals.Research described in chapter 3 demonstrates that,far from being a thing of the past, gender bias inhiring is alive and well in academic environments(Moss-Racusin, Dovidio et al., 2012a).Asian, black, Hispanic, and American Indian/Alaska Native women in academic STEM departmentsface additional challenges. Collecting andaggregating data on specific fields for women ofcolor is difficult because of the low numbers, butwomen of color in academic science and engineeringdepartments generally report different, andmore substantial, stress than do other demographicgroups. For example, women and men of color aremore likely to report the stress of struggling withpersonal finances, women of color report morestress than both white women and men of colorin lack of personal time and managing household

AAUW 37duties, and women of color report the most stressfrom discrimination. Additionally, 79 percent ofwomen of color responded affirmatively to thestatement “I need to work harder to be perceivedas a legitimate scholar,” compared with 67 percentof white women, 60 percent of men of color, and52 percent of white men (National Academy ofSciences, 2013).Stereotypes and biasesStereotypes and biases are important cultural factorsthat may influence women’s representationin engineering and computing. A stereotype is anassociation of specific characteristics with a group(Dovidio et al., 2010). Stereotypes can be descriptive(what women and men are like) or prescriptive(what women and men should be like). Everyoneuses stereotypes to process new informationquickly, assess differences between individuals andgroups, and make predictions. Stereotypes allow usto use fewer cognitive resources than we would ifwe made individual observations each time we metsomeone new (Heilman & Eagly, 2008; Heilman,2012). Indeed, human beings have been describedas “cognitive misers” who are reluctant to engage ineffortful thought unless absolutely necessary (Fiske& Taylor, 1991). For this reason, stereotypes arevery powerful and difficult to override, and theycan lead to biased behavior or discrimination whenwe view members of a group based on their groupstatus rather than as individuals (Heilman, 2012;Dovidio et al., 2010).Gender stereotypes tend to place greater socialvalue on men and evaluate men’s competence asgreater than women’s (Ridgeway 2001). One specificarea in which men are stereotypically deemedmore competent than women is mathematics.Parents’ and teachers’ expectations for children’smathematical achievement are often gender-biasedand can influence children’s attitudes toward math(Gunderson et al., 2012; Varma, 2010). Parents’ andteachers’ own feelings about math can rub off onchildren. In one study, no relationship was foundbetween first and second grade female teachers’math anxiety and their students’ math achievementat the beginning of the school year. By theschool year’s end, however, the more anxious femaleteachers were about math, the more likely girls (butnot boys) in their class were to endorse the commonlyheld stereotype that “boys are good at math,and girls are good at reading” and the lower thesegirls’ math achievement was (Beilock et al., 2010).Warmth versus competenceWhile men are stereotypically thought of as competentin many domains, women are stereotypicallyconsidered to be warm. Competence and warmthare traits that we tend to immediately assign topeople we meet, and these traits are often perceivedto be in opposition to each other (Holoien& Fiske, 2013). Because competence is valued inengineering and computing, the requirements forbeing viewed positively as a technical professionaland being viewed positively as a woman are oftenconflicting. As a result, many women in technicalroles report difficulty forging strong identities asengineers or computing professionals (Hatmaker,2013; Faulkner, 2007, 2009a, 2009b; Ayre et al.,2013), and many female engineers describe anincreased pressure to prove themselves (Hatmaker,2013; Smith, L., 2013). When women emphasizetheir competent characteristics and effectivenessat work, they often experience backlash for violatingthe gender stereotype that women are warm,and they are seen as less likeable than men whoemphasize the same behaviors, especially in maledominatedfields (Phelan et al., 2008; Rudman &Phelan, 2008; Heilman, Wallen et al., 2004). Onthe other hand, women seen as warm but not competentare less likely to be respected and more likelyto be pitied and socially neglected in the workplace(Fiske, 2012; Cuddy et al., 2007).MicroinequitiesBy the time women begin formal engineering orcomputing training in college, they likely haveencountered gender-biased behavior on manyoccasions. Microinequities have been described as“apparently small events … frequently unrecognizedby the perpetrator … which occur whereverpeople are perceived to be ‘different’” (Rowe, 2008,p. 45). Examples include facial expressions, gestures,tone of voice, and subtle actions, such as assigningthe role of note taker to a woman rather than a

38SOLVING THE EQUATIONman. Accumulated over time, these microinequitiescan affect students’ self-concept, which may, inturn, influence their choice of a career (Rowe, 1990;Bandura, 1997).Camacho and Lord (2011) found that femaleengineering undergraduates frequently encountergender-based “microaggressions,” small discriminatorybehaviors of mostly nonphysical aggression(Pierce, 1970), in the engineering educationenvironment. Such behaviors include encounteringsurprise that a woman would be interested in engineering,having male students interrupt or speakover them, experiencing difficulty having their ideasheard, being exposed to sexual discussions andjoking, hearing suggestions that women are in thedepartment only as a result of affirmative actionpolicies rather than because of their achievementsand abilities, and hearing gendered statements byprofessors during lectures. Other research indicatesthat microinequities persist long after women enterthe engineering workforce (Faulkner, 2009b).Microinequities illustrate how discriminationin school and the workplace is often subtleand not overt in its intent to harm (Hebl et al.,2002). Nonetheless, microinequities may result inincreased stress and feelings of exclusion amongwomen in engineering (Camacho & Lord, 2011).Explicit and implicit biasBiases can be explicit (conscious and self-reportedon surveys or in interviews) or implicit (operatingautomatically, typically outside an individual’sconscious awareness). Explicit gender bias has beensteadily declining for decades. Whether due to agenuine increase in egalitarian beliefs or to a greaterhesitation to express biased attitudes (or some combinationof the two), people are less likely today tosay that they hold biased beliefs than they were inthe past (Banaji & Greenwald, 2013). In contrast,implicit gender biases remain pervasive (Nosek,Banaji et al., 2002b; Lane et al., 2012; Smyth,Greenwald et al., 2015; Dovidio, 2001). Even individualswho consciously reject gender stereotypesoften still hold implicit gender biases. In sociallysensitive domains involving topics such as race orgender, some evidence indicates that implicit biasis a better predictor of behavior and judgment thanGamergateAggression against women is not always subtle.The Gamergate controversy of 2014 dramaticallyillustrates the virulence of gender bias in the videogaming industry. The controversy began with an exboyfriend’spostings about the journalistic ethicsof his ex-girlfriend, a prominent game developer.These allegations led to an intense flurry of postingsin online forums and on social media, whichquickly devolved into sexist attacks against thefemale gamer and against women in the industry.Female gamers were barraged by hostile postingsand messages, and some were subjected to threatsof rape and death that resulted in the women fleeingtheir homes. One even received bomb threatsas a result of her work as a feminist critic of gaming.Gamergate is a chilling example of the seriousonline and real-world harassment and aggressionthat some women face in traditionally male technicalrealms.is explicit bias (Greenwald, Poehlman et al., 2009).Implicit and explicit biases are related to each otherbut understood by psychologists to operate via distinctand different psychological mechanisms (DeHouwer et al., 2009; Nosek & Smyth, 2007; Nosek,2007). Because implicit bias is widespread and theprevalence of explicit bias is declining, this chapterfocuses more on implicit bias.The concept of implicit bias was introducedin 1995, when social psychologists AnthonyGreenwald and Mahzarin Banaji built on the psychologicalconcept that our actions are not alwaysunder our conscious control. They argued thatmuch of our behavior is driven by stereotypes thatoperate automatically and, therefore, unconsciously.Researchers theorize that, starting at an early age,we acquire implicit biases simply by living in a societywhere different types of people fill different rolesand jobs (Cvencek, Greenwald et al., 2011; Cvencek& Meltzoff, 2012). Passive exposure to widespreadbeliefs registers these beliefs in our minds withoutour even knowing it. For this reason, implicit

AAUW 39attitudes and beliefs may be better described asreflections of the surrounding environment ratherthan personal attributes (Dasgupta, 2013).Once in place, implicit biases lead us to seekevidence that supports them and question or disregardevidence that contradicts them (Schmader,2013). When we encounter another person, weinstantly view her or him as a woman or man, andour views of any other characteristics that personmay have are shaped by our beliefs about what sheor he is and should be like as a woman or a man(Hassan & Hatmaker, 2014; Ridgeway, 2009). Forexample, a number of qualitative studies conductedin engineering workplaces found that womenare often not seen by their co-workers and colleaguesas full-fledged members of the engineeringprofession (Tonso, 2007; McIlwee & Robinson,1992; Faulkner, 2009b)—they are “highly visible aswomen yet invisible as engineers” (Faulkner, 2009b,p. 169).In 1998 Greenwald and his colleagues introducedthe Implicit Association Test (IAT), a measuredesigned to detect the strength of a person’sautomatic association between two concepts. Todaymany IATs are freely available online at implicit.harvard.edu. One IAT that is especially relevantto women in engineering and computing is thegender-science IAT, which measures the strengthof associations between gender and science. Usinga computer, participants quickly sort words in eachof two conditions: a gender-stereotypical conditionand a counter-stereotypical condition. Inthe stereotypical condition, subjects use the samekeyboard key to categorize items representing male(for example, the word “father”) and science (forexample, the word “physics”) and another key tocategorize items representing female (for example,“mother”) and liberal arts (for example, “literature”).Next, individuals categorize the same words pairedin a counter-stereotypical way, for example, maleand liberal arts sorted with one key and female andscience items with a different key. Which conditionis presented first is randomly varied across participants.A participant’s score is based on the differencein the speed and accuracy of sorting betweenthe two conditions.Both women and men, on average, have a strongtendency on the IAT to more readily associate malewith science and female with humanities than thereverse (Nosek, Banaji et al., 2002a, 2002b; Smyth,Greenwald et al., 2015), and implicit associationsthat pair boys and men with math have been documentedin the United States in children as youngas age 7 (Cvencek, Meltzoff et al., 2011).Guidance counselorsAlthough the relationship between gender and vocationalinterests is complicated, evidence suggests thatcareer inventory surveys currently prevalent in highschool academic and career counseling may have a genderbias. Studies have found that the RIASEC Inventory,the survey most commonly used by career counselorstoday, may be better suited to male students than tofemale students and may lead to different occupationrecommendations for girls and boys (Kantamneni &Fouad, 2011; Armstrong et al., 2010).One study found that a sample of guidance counselorsin Utah perceived the values, interests, and qualities ofstudents differently based on gender. Many counselorsalso showed an “alarming” lack of knowledge about engineeringeducational and career paths and were unpreparedto inform students about engineering opportunities(Iskander, 2013). Other research found that someguidance counselors in the southwest were very muchaware that women are underrepresented in STEM occupationsand that girls are negatively affected by gendersciencestereotypes (Ross, 2012). Understanding moreabout guidance counselors’ gender biases, knowledgeof engineering and computing careers, and awarenessof the influence of gender biases may help identify waysfor them to better help girls make informed educationaland career choices.

40SOLVING THE EQUATIONMost studies that examine the practicalimpact of implicit biases as measured by the IAThave focused on race and not gender (Banaji &Greenwald, 2013). A few examples of the behaviorsfound to be predicted by individuals’ implicitpreference for white people include less comfortand less friendliness when talking with a blackinterviewer than a white interviewer (McConnell& Leibold, 2001), greater readiness to perceiveanger in black faces than white faces (Hugenberg& Bodenhausen, 2003), and greater likelihood tolaugh at racial humor and rate it as funny (Lynch,2010). Green and colleagues (2007) found thatphysicians with greater implicit racial biases favoringwhites recommended optimal treatment foracute cardiac symptoms more often for a whitepatient than for a black patient. These studiesprovide evidence that implicit biases are correlatedwith discriminatory behavior and appear to havereal-world implications.While less research has explored the effectsof implicit gender biases as measured with theIAT, recent evidence described in chapter 3 findsthat implicit gender-math bias is linked to genderdiscrimination. Gender bias coupled with racial/ethnic bias presents a particularly challengingenvironment for women of color in engineeringand computing (Ong, Wright et al., 2011). Furtherstudy is needed about the connection betweenimplicit biases related to women in science andmath as measured with the IAT and actual behaviorstoward women in engineering and computing.Biased evaluationsBiased evaluations play an important role in theprofessional opportunities afforded to women.Even before the formal application process begins,biased evaluations can affect women’s chances ofgetting a position. One study found that professorsfrom many different fields were less likely torespond to an e-mail informally inquiring aboutresearch opportunities from a prospective applicantto a doctoral program if it had a woman’s name onit (Milkman et al., 2014).Hiring situations are particularly vulnerable tobias because hiring managers are generally workingwith limited information under time constraints,and employers typically have little opportunityto reconsider a decision after it has been made(Bendick & Nunes, 2012). Once applicants reachthe interview stage, women in typically male fieldsface additional challenges, such as negative bodylanguage from interviewers, that can affect interviewperformance (Hess, K. P., 2013).Biased evaluations continue to affect womenonce they have been hired. Female managersreceive lower ratings on performance reviews thanmale managers do and are held to a higher standard,needing better performance ratings than theirmale peers to be promoted (Lyness & Heilman,2006). In male-dominated science and engineeringfields women are less likely than men to beseen as experts by their colleagues and to serve inimportant roles on teams ( Joshi, 2014). Managers’discretion over everyday decisions, such as how toexecute company human resource policies, can beinfluenced by gender biases, resulting in diminishedopportunities for women and increased opportunitiesfor men (Roth & Sonnert, 2011; Ayre etal., 2013; Bobbitt-Zeher, 2011; Catalyst, 2008;Fouad et al., 2012; Williams, C. L., et al., 2012).For example, women are less likely than men tobe granted requests for flexible schedules, and thatlack of workplace flexibility can prevent women,especially working mothers, from furthering theircareers (Brescoll, Glass et al., 2013).Castilla and Benard (2010) identified the“paradox of meritocracy,” in which managers inorganizations explicitly identified as meritocraticfavor and reward male employees more generouslythan equally qualified female employees. Thisfinding may have particular relevance for engineeringand computing. More engineers and technicalprofessionals, including organizational leaders,may believe that their workplaces are meritocraticthan do professionals in fields that are less dataoriented.One study of scientists and engineers athigh-tech companies, however, found that womenwere less likely than men to see their workplaces asmeritocracies, perceiving connections to power andinfluence as necessary for advancement (Simard etal., 2008).

AAUW 41In-group favoritismResearch suggests that biased behavior or discriminationtoday most often results from “in-group”favoritism, or giving preferential treatment to otherswith whom we identify in some way, as opposedto negative treatment of “out-group” members ofgroups with whom we don’t identify. Laboratoryand field studies find that discrimination involvingthe absence of positive treatment happens in manyinstances without any accompanying specificallynegative treatment and is, in fact, more commonthan discrimination that involves outright hostility(Greenwald & Pettigrew, 2014). This researchsuggests that in-group favoritism is an importantmechanism by which unequal group outcomes—including unequal outcomes for women—aremaintained and is, therefore, a practice thatindividuals trying to reduce discrimination shouldminimize.Still, if more women moved into leadershiproles in engineering and technical fields, it is possiblethat in-group preferences could result in evenmore women moving into these fields. Kurtulusand Tomaskovic-Devey (2012) found that anincrease in the share of female top managers inan organization was associated with subsequentincreases in the share of women in mid-level managementpositions in that organization, particularlyfemale managers within the same racial/ethnicgroup as that of the top managers.SexismSexism can be either hostile or “benevolent.” Benevolentsexism is rooted in a belief that women needthe help and protection of men (Glick & Fiske,1996; Fiske, 2012). Women who are seen as warmbut not competent are especially likely to be therecipients of benevolent sexist behaviors such asbeing called “sweetheart” or being offered help withdangerous aspects of a job. While on the surfacebenevolent sexism may seem positive towardwomen, its effects are quite the opposite.In one study, participants looked at a jobinterview transcript in which a male interviewershowing hostile sexism (such as a belief thatwomen are incompetent), benevolent sexismEven men are affected bygender biases against womenGender biases can create obstacles not only forwomen in technical workplaces but also for themen who work with them. In one study of equallyperforming teams working on a male-typed task,teams with a higher percentage of women ratedboth their female and male peers’ work more negativelyoverall and expressed less desire to worktogether in the future (West et al., 2012). Anotherstudy found that in a typically male field, peoplerated their male colleagues as less masculine andless deserving of workplace success if they hadfemale supervisors (Brescoll, Uhlmann et al., 2012).This research sheds light on the magnitude of theproblem of gender bias in predominantly male fieldsand perhaps points to one mechanism by which itis maintained. If men’s work is devalued when menwork with women, men might take steps to avoidworking with women, exacerbating the challengesfacing women in male-dominated fields. Whilediversity has demonstrated benefits, there are realchallenges to achieving it.(such as a belief that women should be protected),or no sexism interviewed a female candidate fora stereotypically male job. Researchers found thatthe more participants reported liking the sexistinterviewer, the less competent and deserving ofthe job the participants found the candidate (Good,J. J., & Rudman, 2010). Importantly, observersmore frequently liked the benevolent sexist thanthe hostile sexist interviewer, and observers neednot have held sexist beliefs themselves to like thesexist interviewer.When a leader in an organization is sexist,women can face particularly challenging circumstances.Good and Rudman explain:The more a sexist boss is liked by co-workersand upper level management, the lesscompetent female employees may seem as aresult of his sexist treatment. Because benevolentsexism is often not viewed as sexist

42SOLVING THE EQUATION… and in some cases is viewed as positive,chivalrous behavior … it is plausible thatbenevolent sexists are often viewed morefavorably than hostile sexists, as was the casein the present study. As a result, women maybe especially vulnerable when targeted forbenevolent sexism because the perpetratoris often viewed positively, even though histreatment can undermine female recipients.Benevolent sexism has also been shown to resultin women receiving fewer challenging assignments,which can limit career development and advancement(King et al., 2012). Whether sexist behavior ismore prevalent in engineering or computing workplacesthan elsewhere is not clear. Still, evidenceshows that women experience more sex discriminationin workplaces where they make up less thanone-fourth of the workers (Stainback et al., 2011),and research described in chapter 4 finds that menin engineering and computing fields tend to havehigher explicit and implicit gender-science biasesthan do men in other fields (Smyth, Greenwald etal., 2015).Sexual harassmentSexual harassment, defined broadly as unwelcomeconduct of a sexual nature, can include behaviorssuch as direct and unwanted sexual advances andphysical contact or a hostile work environment thatincludes sexual and sex-based taunting, comments,or denigration (Berdahl, 2007). Sexual harassmentis widespread in engineering and technology(Servon & Visser, 2011; Faulkner, 2009a). Onerecent study of college-educated women in theprivate science, engineering, and technology sectorfound that 63 percent of women in engineeringand 51 percent of women in technology hadexperienced sexual harassment (Hewlett, Sherbinet al., 2014). Organizational climate is a majorfactor in the prevalence of sexual harassment inthe workplace (O’Leary-Kelly et al., 2009). Studiessuggest that male workers in male-dominated fieldsmay harass their female co-workers as a way toprotect their territory when they sense that womenare encroaching on male space (Berdahl, 2007;Chamberlain et al., 2008; McLaughlin et al., 2012).Because of its pervasiveness, sexual harassmentcan seem “normal,” and women may hesitate toreport it, opting instead to employ coping mechanismssuch as tuning it out or thinking of it as anecessary evil. Denissen (2010) documented howwomen in the building trades prioritized maintaininggood relationships with their co-workersabove reporting sexual harassment, attempting toignore the persistent harassing behavior becauseof possible repercussions. In a study of technologyworkplaces, Hunter (2006) found similar challengesfor women, where female employees chose not toreport sexual harassment and tried to downplaytheir femininity to “fit in.”The consequences of sexual harassment aretangible and troubling. Personal or observed experienceswith sexual harassment or gender discriminationare associated with alienation and low jobsatisfaction (Settles, Cortina, Buchanan et al., 2013;Settles, Cortina, Malley et al., 2006). Women whoare targets of workplace incivility such as sexualharassment are more likely to consider quittingtheir jobs and dropping out of their career fields(Cortina, Magley et al., 2001). Sexual harassmentcan affect mental and physical well-being throughincreased stress, anxiety, and depression and loweredself-esteem (Bowling & Beehr, 2006). Theseeffects extend beyond the employees targeted byharassers. Female and male employees who witnessgender-based hostility at work also express greaterorganizational withdrawal and lower well-being(Miner-Rubino & Cortina, 2007).Not all women are equally vulnerable to sexualharassment. Women of color are more likely toexperience sexual harassment as well as racial/ethnic-based harassment (Berdahl & Moore, 2006;Cortina, Kabat-Farr et al., 2013). Additionally,women in positions of authority are more likely toreport harassing behaviors than are women in nonsupervisorypositions, which supports the idea thatsexual harassment may be connected to dominanceand control (McLaughlin et al., 2012; Stainbacket al., 2011; Chamberlain et al., 2008). The negativeeffects of workplace harassment are mitigatedin workplaces where women believe that they havea strong organizational voice (Settles, Cortina,Stewart et al., 2007).

AAUW 43How structural andcultural barriersaffect womenThe factors described above have tangible effects onwomen in engineering and computing. From influencinggirls’ and women’s preferences to their senseof belonging in these fields, cultural and structuralelements, including stereotypes, biases, microinequities,and sexism, shape girls’ and women’s experiencesin engineering and computing.Stereotypes inform preferencesGender biases affect not only how we view andtreat others but also how we view ourselves and thechoices we make about our own futures. From earlychildhood, cultural stereotypes guide our choicesand behavior, steering us toward certain careers thatseem to be the best fit for our interests and abilitiesand away from others. Studies suggest that girlswho associate mathematics with boys and men areless likely to perceive themselves as being interestedin or skilled at mathematics and spend less timestudying or engaging with mathematics concepts.As early as first grade, children have alreadydeveloped a sense of gender identity, and most havedeveloped implicit biases associating boys withmath as well (Cvencek, Meltzoff et al., 2011).As described in chapter 4, individuals’ implicitbiases are related to their college majors, withwomen in science and engineering exhibitingparticularly weak, and men in those fields exhibitingparticularly strong, science-male implicit biases(Smyth, Greenwald et al., 2015; Nosek & Smyth,2011; Lane et al., 2012; Smeding, 2012). Althoughthe causal direction is not known, researchers suspectthat implicit biases likely influence the choicesthat women and men make, while at the same timethe environments in which women and men areimmersed shape their implicit biases.Jacquelynne Eccles, a leading researcher in thefield of occupational choice, has spent the past 35years developing a model and collecting evidenceabout career choice (Eccles [Parsons] et al., 1983;Eccles, 1994, 2007). She found that women areless likely than men to enter occupations such asengineering and computing because they have lessconfidence in their math and physical science abilitiesand because they place less subjective value onthese fields than they place on other occupationalniches (Eccles, 2011b).Many researchers have found a perceived differencein the value that women and men placeon doing work that contributes to society, withwomen, on average, more likely than men to preferwork with a clear social purpose ( Jozefowicz et al.,1993; Margolis, Fisher, & Miller, 2002; Lubinski& Benbow, 2006; Eccles, 2007). A meta-analysisof job-attribute preferences found that the largestgender differences in desired job characteristics arerelated to communal goals, that is, helping otherpeople and working with people, with womenexpressing a greater preference for both (Konradet al., 2000). As described in chapter 6, engineeringand computing careers are perceived by mostpeople as inhibiting communal goals, and individualswho highly endorse communal goals are lesslikely to express interest in these fields (Diekman,Brown et al., 2010).If women perceive engineering and computingas fields that will not allow them to meet highlyvalued goals, it is not surprising that they mightchoose other career paths, even other STEMcareer paths (Benbow, 2012). Eccles and her colleaguesfound that the desire at age 20 to havea job that helps people is a very strong predictorof both women and men completing a major inthe biological rather than the physical sciences ormath and working in biological or medical occupationsrather than physical science or engineeringoccupations at age 25 (Eccles, 2009, 2011a, asreported in Kimmell et al., 2012). In the same vein,Harrison and Klotz (2010) found that the percentageof women in sustainability leader positions indesign and construction companies, a position thatexplicitly connects engineers’ contributions to problemssuch as energy and water resource depletion,climate change, and social inequity, is much higher(39 percent) than the percentage of women ingeneral management positions (8 percent) in thosesame companies.Eccles (2011b) points out that women (andmen) likely do not consider the full range of

44SOLVING THE EQUATIONoptions when choosing a career. Many options maynever be considered because women are unawareof their existence. For example, Google (2014b)identified exposure to computing as a leading factorin women’s choice to pursue computing. Evenwhen girls and women are aware of career options,they may not seriously consider those optionsbecause women have inaccurate informationregarding either the option itself or their ability toachieve in that field. For example, Teague (2002)found that the issues that deter many women frompursuing computing occupations are not supportedby the actual experiences of the women workingthere. Women may not seriously consider othercareers because these options do not fit well withtheir ideas of what is appropriate work for women,further reducing women’s perceptions of the field ofviable options.Focusing on girls’ and women’s choices mightseem to “blame the victim”—women—for theirunderrepresentation in engineering and computing.According to sociologist Maria Charles (2011,p. 25), however, acknowledging gender differencesin educational and career choices doesn’tblame women for women’s underrepresentationin engineering and computing unless preferencesand choices are understood purely as a reflectionof individuals’ intrinsic qualities, separate from thesocial environment in which preferences emerge:The argument that women’s preferencesand choices are partly responsible for sexsegregation doesn’t require that preferencesare innate. Career aspirations are influencedby beliefs about ourselves (what am I goodat and what will I enjoy doing?), beliefsabout others (what will they think of meand how will they respond to my choices?),and beliefs about the purpose of educationaland occupational activities (how do I decidewhat field to pursue?). And these beliefs arepart of our cultural heritage. Sex segregationis an especially resilient form of inequalitybecause people so ardently believe in, enact,and celebrate cultural stereotypes aboutgender difference.Recent analyses of international differencesin the composition of engineering and computingfields make clear that the surroundingculture makes a difference (Frehill & Cohoon,2015; Charles, 2011). While in the United Statesapproximately one-fifth of computer sciencedegrees are awarded to women, in Malaysia womenearn about half of computer science degrees.Similarly, engineering is the most strongly andconsistently male-typed field of study worldwide,but the gender composition of engineering varieswidely across countries. In the United States fewerthan one-fifth of engineering degrees are awardedto women, but in Indonesia women earn just underhalf of engineering degrees. Women make up abouta third of recent engineering graduates in a diversegroup of countries, including Mongolia, Greece,Serbia, Panama, Denmark, Bulgaria, and Malaysia(Charles, 2011).In the United States and other industrializedcountries, individuals and especially girls areencouraged to choose careers based on self-expressionand self-realization, whereas in developingcountries personal economic security and nationaldevelopment are often much more central concernsto young people and their parents. Perhaps ironically,this allows women in countries such as theUnited States more opportunity to conform togender stereotypes in their career choices (Charles& Bradley, 2009; England, 2010).Stereotype threatIn addition to affecting preferences, stereotypesaffect women through a phenomenon known asstereotype threat. Stereotype threat describes athreat—sometimes referred to as an anxiety—thatpeople experience when they fear being judged interms of a group-based stereotype (Steele, 1997;Steele & Aronson, 1995). One need not believethe stereotype nor be worried that it is true ofoneself to experience stereotype threat and itsnegative effects. To be susceptible, individuals mustonly be aware of the stereotype, identify with thegroup that is stereotyped, and care about succeedingin the domain in which the stereotype applies

AAUW 45(Steele, 1997). For this last reason, people whocare the most about succeeding in a domain mayexperience the highest levels of stereotype threat.Robust gender-math stereotypes in U.S. culturemake stereotype threat an important phenomenonin understanding women’s underrepresentation inengineering and computing.Stereotype threat has many negative effects,including physiological stress responses such asa faster heart rate, increased cortisol levels, andincreased skin conductance related to increasedmonitoring of one’s performance and efforts toregulate unwanted negative thoughts and feelings.These extra processes are understood to “hijackcognitive resources” (Schmader & Croft, 2011,p. 792)—specifically working memory capacity—neededfor successful performance (Schmader,2010; Schmader, Forbes et al., 2009; Schmader,Johns et al., 2008; Schmader & Johns, 2003).Stereotype threat has been shown to result indecreased math performance among women (Kochet al., 2014; Spencer et al., 1999; Nguyen & Ryan,2008; Walton & Spencer, 2009; Good, C., Aronsonet al., 2008), decreased interest and motivation inSTEM fields among women (Davies et al., 2002),and decreased sense of belonging (Walton &Cohen, 2007). It ultimately may result in disidentificationwith the stereotyped domain (Steele,Spencer et al., 2002; Steele, 1997). Stereotypethreat can be particularly harmful to women ofcolor because they have to contend with the threatof confirming stereotypes based on both race andgender (Settles, 2004).Stereotype threat is triggered by cues from theenvironment that alert an individual to the possibilityof confirming a negative stereotype about agroup to which she or he belongs. Cues are oftenquite subtle. For example, being a member of aminority group, as women in engineering andcomputing often are, in and of itself can trigger asense of threat. In one study female STEM majorswho viewed a video of a scientific conference withnoticeably more men than women in attendanceexhibited higher indications of stereotype threatand reported a lower sense of belonging and lessdesire to participate in the conference comparedwith women who viewed a similar video with agender-balanced group of attendees (Murphy et al.,2007). In another experiment, subtle sexist behaviorby men triggered stereotype threat in female engineeringmajors resulting in their underperformanceon a math, but not a verbal, test (Logel et al., 2009).Another study found that when watching avideo in which a woman was subjected to dominantbehavior, including command statements like “youneed to...” combined with gesturing and a relaxedposture by a man in a math context, female participantsshowed reduced math performance andreported greater worry about confirming the negativestereotype that women are not as good as menat math. When women watched a video in whichthe man and woman were equal in dominance orthe woman was dominant over the man, however,female participants did not experience stereotypethreat (Van Loo & Rydell, 2014). This last findingdemonstrates the potentially far-reaching benefitsof encouraging equality and female leadershipin the classroom and workplace, because seeingwomen in leadership roles can actually protectother women from the harmful effects of stereotypethreat.Until recently, research on stereotype threatfocused primarily on the effect of stereotype threaton academic performance in the learning environment.Researchers are just beginning to explorethe effects of stereotype threat in the workplace,focusing less on performance measures and moreon measures of psychological disengagement, suchas the degree to which women and men might saythey feel disconnected from their work or mentallyexhausted at the end of the day. A study describedin chapter 5 found that the more female sciencefaculty members discussed research with male colleagues,the more disengaged women felt from theirwork. The more women socialized with their malecolleagues, on the other hand, the more engagedwomen felt with their work (Holleran et al., 2011).The researchers hypothesize that research conversationswith male colleagues may trigger stereotypethreat among female scientists, whereas social conversationsmay increase feelings of belonging and,therefore, reduce experiences of stereotype threat.

46SOLVING THE EQUATIONBecause the effect of stereotype threat in the learningenvironment has been so clearly and repeatedlydemonstrated, it is evident that stereotypescan affect stereotyped individuals in importantways. Understanding how stereotype threat affectswomen in the workplace, especially in fields suchas engineering and computing, is an important areafor future research.Sense of belongingPerhaps because of all these factors taken together,women often report feeling that they don’t belongin engineering and computing fields (Ayre et al.,2013; Faulkner, 2009b). Research described inchapter 8 shows that even among first-year engineeringstudents, women are less likely to perceiveengineering as the right career for them (Cech,Rubineau et al., 2011).A sense of belonging in a particular setting orbroader field is associated with a variety of positiveoutcomes for individuals (Walton, Cohen et al.,2012; Walton & Cohen, 2007). For example, a briefintervention aimed at increasing first-year collegestudents’ sense of social belonging was foundto positively affect participants’ GPA and selfreportedhealth and well-being (Walton & Cohen,2011). Even more relevant, women participants ina social-belonging intervention who learned thatadversities and worries about belonging were commonfor all engineering students raised their engineeringGPAs, improved their academic attitudes,and viewed their daily adversities as more manageable(Walton, Logel et al., 2014). In another study,women showed improved scores on math testsif they wrote a brief essay about social belongingbeforehand (Shnabel et al., 2013). Finally, a seriesof lab studies found that sense of belonging inmath is a good predictor of whether women willcontinue to take math courses (Good, C., Rattan etal., 2012). Sense of belonging can have importanteffects even when individuals are unconscious of it.In some of the above studies, participants indicatedno awareness of the intervention’s impact (Walton& Cohen, 2011).Where do we go from here?The chapters that follow examine specific researchfindings on pivotal issues affecting the representationof women in computing and engineering. Theresults suggest that with small and large changes ineducation and the workplace, progress can be madefor the existing generation of women in these fieldsas well as future generations.

Chapter 3.Gender Bias andEvaluationsWhen I hear the idea that women are “choosing” notto study science, technology, engineering, and math,I think that might be true, but there might be goodreasons they’re choosing not to study these subjects,and one of those reasons could be discrimination.—Ernesto Reuben

48SOLVING THE EQUATIONDespite our best intentions, most of us evaluateand treat women differently than we do men.Evidence shows that bias against women, particularlyin stereotypically male domains, is widespread(Banaji & Greenwald, 2013; Heilman, Wallen etal., 2004). This chapter highlights two studies thatdemonstrate the prevalence and real-world impactof gender bias in hiring decisions. The first studyexamines the impact of gender bias on evaluationsby science faculty, and the second study exploresthe effect of implicit gender bias (bias that operatesautomatically, typically outside an individual’s consciousawareness) on evaluations in the populationmore broadly. Both studies make clear that genderbias affects people’s evaluations of one another and,specifically, hiring decisions.A study of gender biasesamong scientistsScientists by definition strive to be objective. Ifanyone were immune to the effects of gender bias, ascientist would be a likely candidate. Academic scientistsare an especially important group given theirinfluence on the students who will become the nextgeneration of scientists, engineers, and computingprofessionals.Corinne Moss-Racusin, an assistant professorat Skidmore College, and her former colleagues atYale University, John Dovidio, Victoria Brescoll,Mark Graham, and Jo Handelsman, examinedwhether faculty members in biology, chemistry,and physics departments at three public and threeprivate large research universities across the countrymake gender-biased judgments. Keeping the truepurpose of the study hidden, the researchers contactedfaculty members asking for help in developingappropriate mentoring programs for undergraduatescience students. As part of the study,each scientist who agreed to participate was askedto provide feedback on an application for a studentscience-laboratory manager position. Half of thescience professors reviewed an application froma student named “Jennifer,” while the other halfreviewed an identical application from a studentnamed “John.” Moss-Racusin told AAUW:We pre-tested the names and had themrated on trait dimensions, like how intelligentthey sounded, how recognizable, howwarm, how likeable, etc. We chose the namesthat were rated as equivalent, so any differencesbetween the two conditions shouldbe attributable solely to the gender of thestudent and not to any superficial differencesbetween the names.The applicants’ credentials were purposelypresented to describe someone who could quitepossibly be successful as a lab manager but was notan obvious star—a “qualified but not irrefutablyexcellent applicant” (Moss-Racusin, Dovidio et al.,2012b, p. 2). This approach was chosen to replicatethe situation for most aspiring scientists, includingthe type of students most affected by faculty judgmentsand mentoring. The science faculty members(33 women and 94 men) 1 assessed the applicanton competence, hirability, and likeability and toldthe researchers the salary and mentoring that theywould offer the student, with the understandingthat their feedback would be shared with thestudent they had rated (Moss-Racusin, Dovidio etal., 2012b).Pervasive gender discriminationamong science facultyThe results were unequivocal. Scientists, bothwomen and men, viewed the female applicant asless competent and less hirable than the identicalmale applicant and were less willing to mentorthe female candidate than the male candidate (seefigure 14). Faculty members also indicated thatthey would offer less money to the woman thanthe man. Asked to choose a starting salary rangingfrom $15,000 per year to $50,000 per year, scientistsindicated that they would offer an average ofslightly more than $26,500 to the female applicantand an average of slightly more than $30,000 to themale applicant.Although scientists often reported liking thefemale student more than the male student, likingher more did not translate into positive perceptionsof her competence or into material outcomes suchas a job offer, good salary, or career mentoring.

AAUW 49FIGURE 14. FACULTY RATINGS OF LAB MANAGER APPLICANT, BY GENDER OF APPLICANT7WomanMan6Average rating (scale from 1 to 7)543210CompetenceHirabilityMentoringRATING CATEGORIESSource: Moss-Racusin, Dovidio et al. (2012a).The researchers also looked into the underlyingfactors that might influence this behavior andfound that once they controlled for the perceivedcompetence of the applicant, scientists’ preferencefor hiring men disappeared. This finding indicatesthat scientists were less likely to hire a woman thanan identical man because they viewed the woman asless competent, not because of some other potentialreason (such as a perception that a woman wouldbe less committed to the job or more likely to leavethe position).This study provides unique experimental evidencethat science faculty members discriminateagainst female undergraduates. Female and malescientists were equally likely to discriminate againstfemale applicants, and scientific field, age, andtenure status had no effect on the results. Facultyin all scientific fields discriminated against women,women were as likely as men to discriminate, andyounger faculty were as likely as older faculty todiscriminate.Subtle gender bias linkedto discriminationAfter providing feedback on the male or femaleapplicant, the scientists completed the ModernSexism Scale, a commonly used and well-validatedscale that measures modern views toward womenand gender (Swim et al., 1995). This scale isdesigned to detect explicit gender bias focusing onsubtle, contemporary manifestations of bias ratherthan more old-fashioned, overtly hostile attitudestoward women. For example, participants ratedtheir agreement with statements such as “On average,people in our society treat husbands and wivesequally” and “Discrimination against women is nolonger a problem in the United States.”The researchers found that faculty memberswith subtle gender biases made more negativeevaluations of female applicants. That is, the moregender bias the scientists expressed on the ModernSexism Scale, the less competent and hirable theyperceived the female applicant to be and the less

50SOLVING THE EQUATIONmentoring they were willing to offer her. Moss-Racusin explains:We are not talking about faculty membersrefusing to meet with a female student underany circumstances or telling a female studentsomething egregious like “You don’t belonghere, and I would never train you.” Theiroutcomes or behaviors are just slight downgradesof what they would offer the identicalmale student—they’re tweaks. We arenot talking about a $30,000 differential insalary; we are talking about $4,000 per year.… Yet, these subtle downgrades accumulateover time and may come to powerfully shapechoices and careers.How bias affects women inengineering and computingAccording to Moss-Racusin, the findings of thisexperiment are relevant to the engineering andcomputing fields because the biased evaluationsfound here are driven by stereotypes that womenare not well suited to science. Gender stereotypesmay be even stronger in the engineering andcomputing fields, where women are even less wellrepresented than in the scientific fields surveyed inthis study.The differences uncovered in this study couldtranslate into large, real-world disadvantages forwomen in engineering and computing. Becauseconfidence develops, in part, from feedback fromour environment (Bandura, 1982), teacher and facultyassessments of students’ competence contributeto students’ career choices. This study suggests thatgirls and young women are less likely than theirmale peers to receive good evaluations of theircompetence, rewards, and mentoring from theirteachers and professors throughout their STEMeducation, even when their performance is thesame as boys’ and men’s.Biases continue to affect women once theymove into the workplace. From hiring decisionsto project assignments to promotions, the findingsfrom this study reveal that female engineers andcomputing professionals are likely to be evaluatedas less competent, less hirable, and less valuablethan identically qualified male counterparts.Moss-Racusin and her colleagues’ study exposesthe lack of a level playing field in academic science.One might think that science professors, by virtueof their rigorous training in producing objectiveoutcomes, would not be influenced by genderbiases, but this study demonstrates that this issimply not true. Ironically, a belief in one’s objectivitymay increase biased behavior (Uhlmann &Cohen, 2007). Moss-Racusin told AAUW, “We areall—even those of us who are extremely focused onbeing egalitarian—exposed to these pervasive stereotypesthroughout our surrounding culture, andwe’re all fairly affected by them.” Understandingthat bias is widespread is an important first steptoward reducing biased behavior.The biases uncovered in Moss-Racusin and hercolleagues’ study are likely a combination of explicitbiases and implicit biases. The study found anassociation between stated subtle gender bias andlower assessment of women’s competence, suggestingan important effect of explicit bias. At the sametime, gender bias emerged as a general effect in thewhole sample, including the scientists who did notexplicitly express even subtle gender biases, suggestingan important effect of implicit bias operatingmostly beneath the level of consciousness. 2Implicit gender biases anddiscrimination in hiringSome years ago Ernesto Reuben, an economist atColumbia Business School, noticed a number ofarticles on gender differences in personal characteristicssuch as risk-taking, willingness to negotiate,and enjoyment of competition. These articles proposedthat women’s preferences related to factorssuch as these explained women’s underrepresentationin certain positions and fields. Reuben toldAAUW, “After reading these articles, I thoughtthey were missing something. I thought that weneed to be thinking not only about how womenare different from men but about how perceptionscloud judgment and how this can filter back intowomen’s incentives to even try for certain types ofjobs.”Reuben wondered if something was going onin the hiring environment that might help explain

AAUW 51women’s underrepresentation in certain positionsand fields. To explore this question, he and his colleagues,Paola Sapienza from the Kellogg Schoolof Management at Northwestern University andLuigi Zingales from the Booth School of Businessat the University of Chicago, conducted a studyon the practical effects of implicit gender biases onhiring practices (Reuben et al., 2014a, 2014b).A hiring experimentIn a laboratory experiment focused on hiring, justunder 200 undergraduate students—in groups ofaround 14 students each—performed an arithmetictask on a computer, summing as many sets of fourtwo-digit numbers as possible during a four-minuteperiod, and then took part in a hiring exercise.Participants were told that they would be paid asmall amount according to the number of correctanswers they provided and additional money if theywere chosen to be hired. Payment was offered as away to motivate participants to think hard aboutthe questions and to want to be hired, as wouldmost often be the case in an actual hiring scenario.The researchers chose the arithmetic task becauseresearch shows that it is performed equally well bywomen and men while at the same time belongingto an area (mathematics) about which thereremains a pervasive stereotype that men performbetter.Following the arithmetic task, participantswere told the number of problems they had solvedcorrectly. The participants also were told that theywould perform the same task a second time andwere asked to estimate how many questions theywould answer correctly the second time. They alsowere told that their earnings, which were againbased on the number of correct answers theyprovided, would not be affected by the accuracy oftheir estimate.Before the second task began two participantswere chosen randomly to be “job candidates.”They walked to the front of the room carrying“Candidate A” and “Candidate B” signs, while theremaining participants acted as “employers,” taskedwith hiring one of the two candidates to performthe second arithmetic task. If employers chosecorrectly—that is, actually chose the candidate whoperformed better than the other candidate on thesecond arithmetic task—they received increasedcompensation for the study. 3 After the employersmade their hiring decision, the experiment wasrepeated a number of times with other randomlychosen job candidates. Employers chose candidatesfrom pairs representing any combination of womenand men including same-sex pairs to avoid makinggender overly obvious as a factor in employers’ decisions;however, the researchers analyzed data onlyfrom the instances in which the two candidates inthe pair were of different genders.Phase 1: Hiring decisions based on thecandidates’ appearance onlyThe evaluation portion of the study included threephases. In phase 1, employers were asked to choosebetween the two candidates based only on theapplicants’ appearance. As might be expected, whenbasing a hiring decision purely on appearance,employers frequently made bad hiring decisions,selecting the higher-performing candidate onlyabout half (55 percent) of the time (see figure 15).Not surprisingly, the bad hiring decisions werenot gender neutral. Employers were more thantwice as likely to choose the man as the womanwhen they made a bad hiring decision, choosingthe lower-performing man 32 percent of the timeand the lower-performing woman 14 percent ofthe time. Both female and male employers expectedwomen to perform worse than men.Phase 2: Hiring decisions based on thecandidates’ predictions of futureperformanceIn phase 2, employers were shown the candidates’estimates of their performance on the secondarithmetic task. This phase simulated a real-lifeinterview situation in which employers ask—andoften have to take a candidate’s word for—howcandidates would expect to perform in a given job,a scenario especially common in situations wherejob skills are less quantifiable and more subjective.With this limited additional information, theemployers in the study did a better job, choosingthe top performer 69 percent of the time (seefigure 15). Yet in this scenario, when employers

52SOLVING THE EQUATIONFIGURE 15. PROBABILITY OF SELECTING THE BESTCANDIDATE FOR A MATHEMATICAL TASKPercentage10080604020014%32%55%Appearance onlyLower-performingwoman29%69%Candidate'sexpressedanticipatedperformanceLower-performingmanObjectivepast performanceINFORMATION PROVIDED TO EMPLOYERHigher-performingcandidate(woman or man)Notes: The percentage of times that a lower-performing man was chosen in eachcondition was calculated from information provided in figure 1 in the source. Theauthors multiplied the percentage of times the lower-performing candidate was chosenby the percentage of times the lower-performing chosen candidate was a man.Source: Reuben et al. (2014a).2%7%12%81%knew the candidates’ estimated future performance,employers still chose a lower-performing man(over a higher-performing woman) 29 percentof the time—nearly as often as when employersmade their decision based solely on appearance.In contrast, employers chose a lower-performingwoman only 2 percent of the time. In other words,employers made fewer mistakes overall when theyknew candidates’ estimates of their future performance,but when employers did make a bad hiringdecision, they nearly always erred in favor of thelower-performing man and almost never in favor ofthe lower-performing woman.Phase 3: Hiring decisions based onobjective past performanceIn phase 3, employers were told the actual performanceof each candidate on the first task. In thiscase, employers did an even better job, choosing thetop performer 81 percent of the time (see figure15). When employers hired the lower-performingcandidate, they were still nearly twice as likely tohire the lower-performing man over the higherperformingwoman than the reverse, but the genderdiscrepancy was not as great as when employersbased their decisions on candidates’ appearance orcandidates’ future anticipated performance.Implicit biases associated withdiscriminatory behaviorThe researchers also asked all participants to completea math/science-gender Implicit AssociationTest (Greenwald, McGhee et al., 1998). The testresults were clear: Implicit gender biases wererelated to discriminatory hiring practices. The studyfound a strong correlation between implicit math/science-male bias and bad hiring decisions favoringmen.Employers with stronger math/science-malebiases were more likely than others to choose aman rather than a woman in each phase of thestudy. The study found that employers, especiallythose with stronger math/science-male implicitbiases, are likely to rely on their own stereotypesand biases when hiring employees, even whenobjective past performance information is available.Frequently, these biases will lead employers to hirethe less capable candidate.Implications for women in engineeringand computingThis study presents a discouraging scenario forwomen in typically male fields such as engineeringand computing. No matter what type of informationemployers had about applicants, employers’ badhiring decisions usually favored a low-performingmale candidate over a high-performing femalecandidate.

AAUW 53In both the first and second phases of the study,nearly one in three times the higher-performingwoman did not get hired. The odds of higher-performingmen getting hired were much better thanhigher-performing women getting hired in everyscenario, especially when employers based theirdecisions on candidates’ predictions of their futureperformance.One might think that this study overestimatesthe effect of biases on hiring because employersundoubtedly have experience with hiring thatshould give them an edge in accurately evaluatingcandidates over the student employers in the experiment.Real-world employers also have much moreat stake and, therefore, should be more inclined toseek information about past performance (such asreferences). While true, at the same time, this studymay underestimate the effect of biases on hiring,since past performance is likely to be even moreambiguous in reality than in this experiment, andstereotypes thrive in cases where qualifications areambiguous. Reuben told AAUW that he believesthe findings from this study are quite relevant toreal-world situations:These implicit associations that we aremeasuring or capturing—these are not fromthe lab. They were not created in the lab. Weare just putting study participants in a contextwhere they have to make choices, andthey do so based on their biases. I don’t seewhy the biased decisions we see in the labwouldn’t translate to real decisions outside.The advantage of objectiveinformationThis study provides clues about reducing theeffect of gender bias on hiring. The more objectiveinformation employers had to go on, the better andless-biased decisions they made. Specifically, whenemployers had information about applicants’ pastmath performance, they chose the top-performingcandidate 81 percent of the time. In contrast,employers chose the top performer only 55 percentand 69 percent in phases one and two of thestudy, when they had less-objective performanceinformation on which to base their decisions. Theresearchers found that self-reported informationdoesn’t help as much as objective informationbecause “men tend to be more self-promotingthan women in these reports but employers … donot fully appreciate the extent of this difference”(Reuben et al., 2014a, p. 4408).While objective information helps, it doesn’tfully overcome the problem of gender-biased hiring.Even when participant employers had objectiveinformation on the past performance of candidates,they chose to hire the lower performer one in everyfive times, and when they did, they were twice aslikely to hire the lower-performing man as thelower-performing woman. This gender gap in hiringdecisions is due to a systematic underestimationof the performance of women compared with men(Reuben et al., 2014b). While it can be difficult tolearn objective information about a job applicant’spast performance that is relevant for the job she orhe is seeking, the findings from this study suggestthat the more employers can base their hiringdecisions on objective information, the better—andless-biased—decisions they will make.What can we do?Chapter 10 includes recommendations, basedon the large body of gender-bias literature, forcounteracting the harmful effects of gender bias.From these two studies, several recommendationsare clear.A first step toward addressing biases in hiring isacknowledging the reality that we are all influencedby gender biases, whether or not we consciouslyendorse them. Reuben told AAUW that, for themost part, “we are not trying to convince peoplethat discrimination is bad anymore. Now we’re justtrying to make people aware that they might bediscriminating.” Requiring researchers who receivefederal funds to participate in training on how tocounteract the harmful effects of bias could helpincrease awareness. One practical way to do thisis to incorporate bias training into the responsibleconduct of research (RCR) training alreadyrequired for individuals, including many academic

54SOLVING THE EQUATIONscientists, who work on federally funded researchprojects. Institutions should consider requiring biastraining for all administrators as well.Second, Reuben and his colleagues found thatemployers made better and less-biased hiringdecisions when they were provided with objectiveinformation about past performance. Employersshould base hiring decisions on objective past performanceinformation as much as possible.Third, more research is needed to establishthe effectiveness of interventions aimed at reducingboth implicit and explicit bias, particularlyresearch in applied settings including workplacesthat employ engineers and computing professionals.Moss-Racusin and her colleagues recentlypublished recommendations for the design andevaluation of bias-reduction interventions to ensurethat they are evidence based and scientifically validated(Moss-Racusin, van der Toorn et al., 2014).She told AAUW:I hope that in 10 years we will have sometheory-driven, solid, validated programs forprejudice reduction and boosting diversity inSTEM. I hope that we will be able to showthat if we administer interventions underthese circumstances to these folks, we aremost likely to see improvement. I hope thatwe will have a deep tool kit, that we willknow, “for this kind of audience this wouldbe best; for that kind of audience that wouldbe best.” I don’t think we are there yet, butwe are moving there.Chapter 3 notes1. The demographic breakdown of the study’s sample is consistent with the demographic breakdown of the population of scienceprofessors in the United States overall. Because of this, the results of this study can be generalized, with some confidence, to thebroader population of science professors.2. Although in their study Moss-Racusin and her colleagues did not directly assess the science faculty members’ implicit genderbiases (for example, by administering an Implicit Association Test) for feasibility reasons, she told AAUW, “It would be veryinteresting to see if implicit gender bias is linked to effects in the same way that the modern sexism scale is.” Determining therelative impact of explicit biases compared with implicit biases on evaluations is an important question for future research.3. One advantage of this experimental design is that it narrows the factors influencing employers’ decisions to perceptions of competencealone. Extraneous factors such as employers’ concerns that women will drop out of the labor force or not be as committedto their jobs shouldn’t have influenced the student employers’ decisions because they were tasked only with hiring the candidatebest able to conduct a four-minute math task within the time frame of the session.

Chapter 4.Gender Bias andSelf-ConceptsThere is a huge difference between the gender-scienceassociations in the minds of male and female scientists… and we have much to learn about how this gap mayaffect learning and work environments.—Frederick Smyth

56SOLVING THE EQUATIONImplicit biases are associated not only with howwe evaluate and treat others but also with attitudesand outcomes about our own future (Smyth,Greenwald et al., 2015; Nosek, Banaji et al., 2002a).Researchers hypothesize that the influence goesin both directions: Implicit biases shape outcomesand actions (such as majoring in engineering orcomputing), and experiences (such as majoring inengineering or computing) shape implicit biases(Nosek & Smyth, 2011). Women and men areexposed to the same stereotypes about women inmath and science in U.S. culture and, on average,acquire the same implicit or unconscious “science/math=male”biases by age 7 or 8 (Cvencek,Meltzoff et al., 2011).Three long-time researchers of implicit bias,professors Brian Nosek and Frederick Smyth atthe University of Virginia and professor AnthonyGreenwald at the University of Washington,examined the relationship between gender-scienceimplicit biases and engineering and computingoutcomes, specifically college major, for womenand men. The study’s sample included more than175,000 participants who visited the publicly availableProject Implicit website (implicit.harvard.edu) 1between May 2004 and January 2012. The medianparticipant age was 25, 70 percent were women,more than three-quarters were white, and all had atleast some college education.Associating sciencewith maleAt the Project Implicit website, participants indicatedtheir current or past college major, answeredquestions about their explicit gender-science associations,and completed the gender-science ImplicitAssociation Test (IAT), which tested how rapidlyparticipants associated science with male and sciencewith female. A score of zero indicates that theparticipant was equally fast categorizing sciencewith male and science with female. Positive valuesindicate a stronger association (or faster categorization)of science with male than with female, 2and negative values indicate a stronger associationof science with female than with male (Smyth,Greenwald et al., 2015).Implicit science-male biasOverall, women and men both exhibited a similarstrong association of science with male. Whenthe researchers broke down the data by collegemajor, however, striking results emerged. Men whomajored in scientific fields such as engineering orcomputing had strong implicit science-male biases,while women who majored in those fields had weakscience-male implicit biases. 3 In the least scientificfields, such as the visual and performing arts andhumanities, this situation was reversed: Womenheld much stronger science-male implicit biasesthan men did (see figure 16). Smyth told AAUW,“This big difference in implicit bias between fieldsis compelling evidence that these implicit associationsare not a one-size-fits-all. Implicit bias is notsomething that is intractable, that cannot move.”In the most scientific fields (including engineeringand computing), men held much strongerscience-male implicit biases than women did.“Those gender differences are in the realm of thebiggest cognitive gender differences that we eversee,” said Smyth. Men in science and women inthe humanities (those in gender-traditional fields)had the strongest biases while the weakest sciencemalebiases were found among women and men ingender-nontraditional fields.Notably, female engineers and computer scientistsdid not show a reversal in typical implicitbiases; that is, they did not more strongly associatescience with female than with male. Smyth toldAAUW:I see it working like this: A man in sciencegets up every morning, looks in the mirror,and if he’s feeling good about his scientificachievement lately, the connection betweenmaleness and science is reinforced. Thisprocess need have nothing to do with thinkingdisparagingly about women in science.Unconsciously, routinely, this provides steadyfuel to associations between maleness and“scienceness” for him. For women, the samething happens, their science and femaleassociations are routinely stoked, but theystill are in this culture where there are somany men and still a great deal of stereotyping,so the averages for the women don’t

AAUW 57FIGURE 16. AVERAGE SCIENCE-MALE IMPLICIT ASSOCIATION TEST SCORE,BY GENDER AND COLLEGE MAJOR1.4WomenMen1.2Implicit science-male score(standard deviations from 0)1.00.80.60.40.20.0Visual/performing artsHumanitiesLaw/legal studiesCommunicationEducationSocial science/historyMAJOR FIELDBusinessPsychologyHealth sciencesComputer/information scienceBiology/life sciencesEngineering/math/physical sciencesNotes: Majors are ordered from left to right by ratings of science content. A score of 0 indicates no science-male implicit bias. Lower numbers on the y axis represent lowerscience-male implicit bias scores.Source: Smyth, Greenwald et al. (2015). Adapted with permission from Frederick L. Smyth.move into the science-is-female portion ofthe chart. They’re still solidly in the scienceis-maleportion.Yet even though women in engineering andcomputing associated science with male morereadily than science with female, they had weakerimplicit science-male biases than women in nonscientificfields had. Supporting this finding, anotherstudy showed that women with weaker math-maleimplicit biases reported more participation in math,more self-ascribed mathematical skill, and higherscores on the SAT and ACT math tests comparedwith women who had stronger math-male implicitbiases (Nosek & Smyth, 2011). Study after studyhas found that women in engineering and computingtend to have weak implicit associations betweenscience/math and male (Nosek & Smyth, 2011;Smyth, Greenwald et al., 2015; Smyth, Guilford etal., 2011).These findings cannot explain which camefirst—implicit bias or college major choice. Doweak implicit science-male biases lead womento major in engineering or computing, or whenwomen major in engineering or computing, dotheir science-male biases weaken? This researchdoesn’t answer these questions, but researcherssuspect that both happen. Implicit biases probably

58SOLVING THE EQUATIONImplicit bias is a goodpredictor of college majorImplicit science-male bias has been found to bea stronger predictor of majoring in a STEM fieldthan has explicit bias (Nosek & Smyth, 2011; Nosek,Banaji et al., 2002a) or an individual’s score on themath SAT, an indicator often used to identify potentialfor achievement in these areas. The correlationis in opposite directions for women and men.For women, weaker science-male bias is associatedwith majoring in STEM fields, whereas formen, stronger science-male bias is associated withmajoring in these fields. Importantly, the correlationbetween level of implicit bias and majoring ina STEM field is as powerful for individuals who testhighest on standardized math tests as for thosewho test lowest.Smyth and his colleagues were not surprised tofind that implicit biases more accurately predictedmajoring in a STEM field than explicit biases didbecause the biases we report are subject to ourown limited abilities to see ourselves clearly andmotivations to present ourselves in a favorablelight. Implicit biases, in contrast, are more difficultto mask.influence the choices that women make, while atthe same time, the environments in which womenare immersed shape their implicit biases (Nosek& Smyth, 2011). Regardless of the causal direction,these studies illuminate a strong relationshipbetween implicit biases and the pursuit andattainment of science, engineering, and computingdegrees—positive for men and negative for women.Explicit science-male biasThis study also found that men who had majored inengineering, computing, math, or a physical sciencereported the highest levels of explicit science-malebias as well. On a questionnaire, on average, menwho majored in scientific fields such as engineeringand computing reported stronger consciousscience-male associations than other women andmen did, including women in engineering andcomputing (see figure 17). One possible explanationfor this is that some of the explicit associationspeople report are based on dominant patternsin their environment. For example, as shown infigure 17, women in health sciences and biologyhave lower explicit science-male biases than otherwomen do, probably because male-to-female ratiosare much lower in health and biological sciencesthan in other scientific fields. In engineering andcomputing, on the other hand, male-to-femaleratios are much higher than in other fields, andmen in these fields report higher science-malebiases than other men do.How gender biases affectengineering and computingenvironmentsIn a sense these findings are not particularlysurprising. It is logical that men who major inengineering, computing, and science associatethemselves with science and so more readilyassociate science with men more generally. By thesame token, it is logical that women who majorin engineering, computing, and science associatethemselves with these fields and so are less likelyto readily associate science with men. Yet eventhough these findings are not particularly surprising,they beg an important question: What is theimpact on women’s representation in engineeringand computing of classrooms and workplaces madeup primarily of men with strong math-male andscience-male implicit biases? According to Smyth,we still have a lot to learn about that.While ample evidence shows that implicit biasinfluences perceptions of women in science andmath, little research specifically links IAT scoreswith discriminatory behavior in the area of womenin engineering and computing. Still, researchdescribed in chapter 3 provides some evidence thatimplicit math/science-male biases as measured bythe IAT are associated with discriminatory behaviortoward women (Reuben et al., 2014a).The research of Smyth and his colleaguesfound that men who majored in engineering

AAUW 59FIGURE 17. AVERAGE SCIENCE-MALE EXPLICIT ASSOCIATION SCORE, BY GENDER AND COLLEGE MAJOR1.6WomenMen1.41.2Explicit science-male score(standard deviations from zero)1.00.80.60.40.20.0Visual/performing artsHumanitiesLaw/legal studiesCommunicationEducationSocial science/historyMAJOR FIELDBusinessPsychologyHealth sciencesComputer/information scienceBiology/life sciencesEngineering/math/physical sciencesNotes: Majors are ordered from left to right by ratings of science content. A score of 0 indicates no science-male explicit bias. Lower numbers on the y axis represent lowerscience-male explicit bias scores.Source: Smyth, Greenwald et al. (2015). Adapted with permission from Frederick L. Smyth.and computing had some of the highest levels ofscience-male implicit biases of anyone majoring inany field and the highest levels of explicit sciencemalebiases of all (Smyth, Greenwald et al., 2015).Smyth told AAUW, “Should we be very afraid? Idon’t think so, any more than we should alreadybe working hard to create inviting, welcomingenvironments and continuing to learn as much aswe can about what facilitates a sense of belongingamong people who don’t feel as well represented ina particular domain.”Reducing discriminatory effectsof implicit biasesReducing possible discriminatory effects of implicitbiases requires organizations to institute policiesand practices that make hiring and promotioncriteria explicit, because without clearly definedcriteria, implicit bias can easily creep into decisionmaking. According to Smyth:Our best advice for counteracting theeffects of implicit bias is for organizationsto do sustained work to create welcoming,

60SOLVING THE EQUATIONequality-focused environments. Educatingpeople about implicit bias, giving them asense of the literature and some of these fascinatingstudies in itself accomplishes little.Following the advice of Tony Greenwaldand Mahzarin Banaji in their book Blindspot[Banaji & Greenwald, 2013], we recommendthat organizations put into place “nobrainers”that minimize subjective aspectsof decision making about people, becausewe are always at risk of being influenced bybiases that we don’t know we have.Putting in place “no-brainers” means removingopportunities for bias to influence our decisions.For example, as much as possible, organizationsshould remove information about an individual’sage, race, or gender from decision-making contexts.Smyth notes that it takes concerted effortto create and maintain environments that arewelcoming:If you look around and see portraits on thewall in the engineering conference roomthat are 19 men and one woman, thatsends a message that this is a male bastion.Organizations can think creatively abouthonoring their legacy of male leaders with acontemporary reality that includes women.It’s tricky, and we need to be always thinkingcreatively because there isn’t any clear, slamdunkmethod that’s yet been developed forchanging implicit biases.Changing implicit biasesThe idea of changing implicit biases indeed seemsto be a formidable task. How can you change somethingyou’re not even aware that you have? Yetwomen in engineering and computing have weakerscience-male implicit biases than women in nonscientificfields do. Therefore, reducing math-male andscience-male implicit biases, especially among girls,seems like an important avenue to explore to makeengineering and computing careers true optionsfor girls and women. Fortunately, because implicitattitudes are linked to the environment, researcherstheorize that “such biases should shift when peopleare immersed in different types of situations wherethey encounter admired and counter stereotypicalindividuals who do not fit their prescribed role insociety” (Dasgupta, 2013, p. 241).One recent experiment provides support forthe notion that implicit biases are indeed malleable.Nilanjana Dasgupta, a psychology professorat the University of Massachusetts, Amherst,and her colleagues found that college women whohad a female as opposed to a male instructor fora calculus course had significantly more positiveimplicit attitudes toward math and strongerimplicit self-identification with math after thecourse than before the course (Stout, Dasgupta etal., 2011). 4 Women’s explicit beliefs about math didnot change even though their implicit associationsdid. Dasgupta (2013, p. 264) explained:In talking to participants at the end of thestudy, it was eminently clear that thesewomen were unaware that the people theycame in contact with in class … had anyeffect on their own academic self-conceptand career goals. Like most people, participantsdescribed their academic interests asdriven mostly by their intrinsic interest andmotivation. They were unaware of the profoundeffects their local environments werehaving on their intellectual self-concept andcareer trajectories.Importantly, while women had more positiveimplicit attitudes toward math and strongerimplicit self-identification with math after taking acalculus course taught by a woman, women’s mathmaleimplicit biases did not change. Nevertheless,according to Smyth, the changes in attitudes andself-identification are theoretically important precursorsto changes in math-male implicit biases:Changes in implicit attitudes toward mathand self-identification with math are relatedto changes in implicit gender biases towardmath. If we make substantial changes inimplicit self-concept about a domain [in thiscase, mathematics], then we ought to see acorresponding change in the stereotype.The message from Dasgupta’s study is thatfemale role models, specifically teachers, can changegirls’ and women’s attitudes and identification

AAUW 61with math and science. Researchers suggest thatthese changes will ultimately lead to changes inimplicit biases. Other research by Dasgupta andher colleagues clarifies that relatability is a necessaryelement for role models to be effective. Femaleexperts portrayed as “superstars” who are uniqueand exceptional have little impact—and sometimeshave a deflating effect—on young women’s viewsof themselves. Likewise, role models with whomyoung women do not identify have little effect onwomen’s self-concepts even if they are in frequentcontact. Dasgupta’s research suggests that changingimplicit self-concepts (and eventually implicitbiases) requires both frequent exposure to femalerole models as well as feeling a connection withthese role models (Asgari et al., 2010; Dasgupta,2013).What can we do?The effect on the engineering and computingworkplace and education environments of so manymen with strong science-male implicit biases isunknown and is an area ripe for future research.Most women in engineering and computing, incontrast to their male colleagues, tend to have relativelyweak science-male implicit biases. Becauseof this, it makes sense to consider ways to reducegirls’ and women’s science-male implicit biases as apotential way to increase the chances that girls andwomen will develop an interest in these fields.While research has yet to identify a clear-cutmethod for reducing implicit biases, female rolemodels with whom women can identify may help.Female role models have been shown to strengthenyoung women’s math attitudes and self-concepts—precursors to reducing gender-math and genderscienceimplicit biases—and increase girls’ andwomen’s abilities to truly consider engineeringand computing fields as viable career options. Inaddition, organizations should create welcomingenvironments for women. This includes activelydeveloping female leaders and training managersto run productive, inclusive teams. Organizationsshould also make hiring and promotion criteriaexplicit and examine these criteria for implicitbiases. Finally, organizations should make the firstround of hiring, awards, and promotion discussionsgender-, disability-, and race-blind when possible.Chapter 4 notes1. Project Implicit is a popular website at which visitors can participate in studies and learn about implicit social cognition relatedto a variety of topics. Thousands of individuals visit the website each week. While participants in this study were not randomlychosen, Smyth told AAUW that he’s conducted similar, smaller studies with representative samples of participants and hasconsistently found patterns identical to that shown here, strengthening the credibility of this pattern.2. A science-male IAT score could just as accurately be called a liberal arts-female score since both stereotypes are combined in IATmethodology.3. Nosek & Smyth (2011) found similar results using the gender-math IAT.4. Participants’ implicit attitude toward math was assessed by measuring how quickly participants categorized words related tomath, such as “algebra” or “equation,” with positive versus negative words, such as “joy” or “filth.” Participants’ implicit self-identificationwith math was assessed by measuring how quickly participants paired the same math words with first-person pronouns,such as “me” or “myself,” compared with third-person pronouns, such as “they” or “them” (Stout, Dasgupta et al., 2011).

Chapter 5.Stereotype Threat inthe WorkplaceAfter women run the gauntlet of standardized testingand make it into graduate programs or even careers inSTEM, they still find themselves in contexts where theconcern that they might be judged through the lens of astereotype can rear its head.—Toni Schmader

64SOLVING THE EQUATIONWomen presented with the stereotype that menare better at math perform worse on difficult mathtests than women who are told that there are nogender differences in math performance (Spenceret al., 1999). This lowered performance is a resultof a phenomenon called “stereotype threat,” ora fear of confirming a negative stereotype aboutyour group (Steele, 1997). Hundreds of studieshave verified the influence of stereotype threat inmany domains, including academic performanceamong black students (Steele & Aronson, 1995;Taylor & Walton, 2011), memory in older adults(Hess, T. M., et al., 2003), girls’ chess performance(Rothgerber & Wolsiefer, 2014), and women’sathletic performance (Hively & El-Alayli, 2014). Inevery case even subtle reminders of negative stereotypescan have an impact on performance, sometimesin dramatic ways. For example, accordingto a meta-analysis by Walton and Spencer (2009),stereotype threat results in an underestimation ofthe intellectual ability of black and Latino studentsby approximately 40 points on the SAT math andreading tests.Stereotype threat is thought to undermineperformance in part by reducing working memorycapacity as individuals use some of their finitecognitive resources to suppress negative emotionsor knowledge of a stereotype to focus on a task(Croizet et al., 2004; Schmader & Johns, 2003).Stereotype threat has been shown to increase stressand anxiety (Blascovich et al., 2001; Bosson et al.,2004; Inzlicht & Ben-Zeev, 2003) and is theorizedto ultimately lead to disidentification and disengagementfrom domains in which a person feelsstereotyped (Steele, 1997).A study by University of Waterloo psychologyprofessor Christine Logel and her colleagues(2009) explored whether interpersonal interactionsoutside the classroom can create stereotype threatconditions for women in a male-stereotyped field.The researchers found that interacting with subtlysexist male peers caused women who were majoringin math, science, or engineering to experiencestereotype threat. Men holding sexist attitudesrevealed their sexism through subtle but consistentbehaviors, including behaving in more dominantand flirtatious ways. After interactions with thesemen, women subsequently performed worse on anengineering or math test (but not an English test)than did women who interacted with nonsexistmen. 1How stereotype threataffects women in theworkplaceResearchers have only recently begun to examinehow stereotype threat operates in the workplace(Kalokerinos et al., 2014). Most research to dateon stereotype threat has focused on academicperformance (Walton & Spencer, 2009). By virtueof its relationship with stress, anxiety, and disengagement,however, stereotype threat could alsolead to a wide variety of negative experiences forstereotyped individuals at work. Engineering andcomputing workplaces, in which women potentiallyface an almost constant threat of confirming thestereotype that men are better suited for scienceand math, are prime work environments in whichto explore stereotype threat.Toni Schmader, a psychologist at the Universityof British Columbia, and her colleagues ShannonHolleran, Jessica Whitehead, and Matthias Mehlare among the first researchers to study stereotypethreat in the workplace. Moving beyond strictissues of performance, these researchers conductedan experiment that explored whether experiencesof stereotype threat might relate to disidentificationor disengagement in the workplace. In aninterview with AAUW, Schmader explained howthey shifted their focus from the classroom to theprofessional field:As we tried to understand the lower representationof women in science and technologyfields more generally, it became apractical but also a theoretically interestingquestion as to why the effects of stereotypethreat would be limited to the earlier stagesof the pipeline, when people are getting theireducation and their credentials. There arereasons why you might think that peoplewould have good coping strategies for dealingwith stereotype threat by the time theyget to a professional environment. But we

AAUW 65suspected that there were still ways in whichstereotype threat would be experienced inworkplace environments, and so we set outto test that.Moving from the laboratory to the workplacemeant that Schmader and her colleagues needed anew way to identify when female scientists experiencedstereotype threat in the workplace and howto measure changes in performance. The researchershypothesized that stereotype threat could comeinto play for female science and engineering facultywhen they talked about their research with malecolleagues. Schmader told AAUW why they testedstereotype threat within conversations:Science faculty members don’t take standardizedtests on a regular basis. So wheredo we feel like our ideas are tested? They’reobviously tested in reviews of articlessubmitted to journals, but they can also betested in conversational contexts, and that’swhat led us to look at conversations themselvesas potential triggers of stereotypethreat.To capture whether conversations could triggerstereotype threat, 37 faculty members fromSTEM departments at a large public universitycarried portable recording devices for four days.The devices recorded 50 seconds of conversationevery 9 minutes from 6 a.m. to 11 p.m. Theresearch team transcribed the resulting conversationsthat took place between colleagues during thework day, coding the gender of the speakers andwhether the conversation concerned research orsocial matters. Two researchers also independentlyrated each conversational snippet for the speaker’slevel of competence, likeability, and contribution tothe conversation. Researchers combined conversationdata with a questionnaire on job engagement(Holleran et al., 2011).Research conversations andstereotype threatSchmader and her colleagues found that the morewomen discussed research with male colleagues, theless engaged they reported feeling with their work.Men, on the other hand, showed the expectedrelationship of being more engaged with their workwhen they had more conversations about researchwith their male colleagues (see figure 18). 2 In addition,researchers coded the female scientists, onaverage, as sounding less competent than their malepeers during research conversations with othermale colleagues. The researchers found no genderdifferences in perceived competence during anyother situations.The relationship between social conversationsand engagement also differed by gender. In contrastto research conversations, social conversations withmale colleagues were related to more engagementfor female scientists (see figure 19). Social conversationsbetween two male colleagues or two femalecolleagues, on the other hand, were related to lessengagement, which is what one might expect sincemore time socializing at work means less timeworking.While researchers cannot draw definitiveconclusions from their correlational data, Schmaderand her colleagues hypothesize that research conversationswith men may cue stereotype threat forfemale scientists. Social conversations with malecolleagues, on the other hand, do not appear toactivate a negative stereotype of women in STEMand may actually lessen the threat women feel inmale-dominated environments by increasing a feelingof belonging (Holleran et al., 2011). Far fromthe last word on the subject, these findings point toadditional questions about how stereotype threatoperates and affects women in typically male worksettings.Other research supports the potential forstereotype threat and negative work outcomeswhen women compare themselves to men in theworkplace. Courtney von Hippel, a senior lecturerin psychology at the University of Queensland inAustralia, and her colleagues conducted studiesof stereotype threat in the workplace that foundthat women who compared themselves to menin the same organization experienced stereotypethreat (von Hippel, Issa et al., 2011). Experiencesof stereotype threat, in turn, were associated witha separation of a “female” identity and a “work”identity (von Hippel, Walsh et al., 2011), decreased

66SOLVING THE EQUATIONFIGURE 18. RESEARCH CONVERSATIONS WITHMALE COLLEAGUES AND LEVEL OF JOBDISENGAGEMENT, BY GENDERFIGURE 19. SOCIAL CONVERSATIONS WITHMALE COLLEAGUES AND LEVEL OF JOBDISENGAGEMENT, BY GENDER2.502.25WomenMen2.502.25WomenMenDisengagement from job(scale from 1 to 4)2.001.751.501.25Disengagement from job(scale from 1 to 4)2.001.751.501.251.00Fewer conversationsMore conversations1.00Fewer conversationsMore conversationsRESEARCH CONVERSATIONS WITH MALE COLLEAGUESSOCIAL CONVERSATIONS WITH MALE COLLEAGUESNotes: On average, study participants’ conversations with their male colleagues wereabout research 47 percent of the time. The “fewer” value represents 19 percent, in otherwords, study participants who talked about research 19 percent of the time when theyspoke with their male colleagues. The “more” value represents 77 percent, in otherwords, women and men who talked about research 77 percent of the time theyconversed with their male colleagues. Nineteen percent is one standard deviation lowerthan the average value, and 77 percent is one standard deviation higher than theaverage value (AAUW communication with Toni Schmader).Source: Holleran et al. (2011).Notes: On average, study participants’ conversations with their male colleagues wereabout social topics 23 percent of the time. The “fewer” value represents 5.5 percent, inother words, study participants who talked about social topics 5.5 percent of the timewhen they spoke with their male colleagues. The “more” value of social conversationswith male colleagues represents 40.5 percent, in other words, study participants whotalked about social topics 40.5 percent of the time when they conversed with theirmale colleagues. Five and one-half percent is one standard deviation lower than theaverage value, and 40.5 percent is one standard deviation higher than the average value(AAUW communication with Toni Schmader).Source: Holleran et al. (2011).perceived likelihood of achieving career goals,reduced job satisfaction, and greater intentions toquit (von Hippel, Issa et al., 2011).Limitations and upcomingresearchUnderstanding the implications of stereotypethreat in the workplace is important. Stereotypethreat may be a key to why many women leaveengineering and computing jobs, since disengagementis theorized to be a hallmark response tostereotype threat over time (Aronson et al., 2002;Steele, 1997). To gain a better understanding ofhow stereotype threat affects women in typicallymale work environments, Schmader and her colleaguesare now exploring stereotype threat amongprofessional engineers. This research will add totheir earlier findings by specifically measuring professionalengineers’ concern about being evaluatedbased on their gender. Preliminary findings suggestthat conversations with male colleagues that cuefeelings of inauthenticity or a lack of acceptanceare associated with an increase in gender awareness,which in turn correlates with higher levelsof mental exhaustion and lower job commitment(Hall et al., in press). Understanding more abouthow stereotype threat operates in the workplaceis an important area for future investigation asresearchers continue to explore the factors behindthe underrepresentation of women in engineeringand computing.What can we do?Schmader’s ongoing research is finding thatgender-inclusive policies are associated with fewerexperiences of stereotype threat as well as greaterorganizational commitment and life satisfactionamong female professional engineers (Hall et al.,in press). Examples of gender-inclusive policies

AAUW 67include efforts to reduce sexual harassment at work,policies that reduce work-family conflict, and useof gender-inclusive language. Employers shouldbe careful about the language used in job postingsbecause language can make a difference in whoapplies for positions. Job postings and descriptionsshould be gender neutral and use wordsthat include feminine strengths and skill sets. Jobadvertisements, mission statements, and internalcommunications should explicitly convey that anorganization values diversity and gender inclusiveness.Finally, increasing the number of womenin the workplace at all levels of management canreduce the prevalence of stereotype threat. Researchsuggests that stereotypes are activated for womenmore frequently when men significantly outnumberwomen (Inzlicht & Ben-Zeev, 2000; Murphy et al.,2007).Chapter 5 notes1. Logel and her colleagues inferred that women experienced stereotype threat in this experiment because women reported suppressingconcerns about gender stereotypes. Suppressing these concerns is an established mechanism of stereotype threat that canlead to lowered performance and other outcomes.2. Schmader and her colleagues found no significant relationship between engagement and research conversations with female colleagues,although conclusions were tempered by the low number of conversations between female colleagues in the study.

Chapter 6.Making the Worlda Better PlaceCommunal goals are highly valued generally by society.While we have good reason to suspect that bringing in thecommunal aspect of engineering and computing mightbe especially effective at attracting and retaining girlsand women, we have also never shown that it dissuadesmen or boys. To me that speaks to the potential powerof this approach as a lever to bring more people intothese fields.—Amanda Diekman

70SOLVING THE EQUATIONSome researchers argue that the underrepresentationof women in engineering and computing maybe explained in part by the perception that thesefields lack an emphasis on communal goals. Inother words, because engineering and computingoccupations seem less likely than other professionalfields to involve collaboration or a direct benefitto others, they may be less appealing to manywomen—and to many men as well.Two studies led by Miami University psychologyprofessor Amanda Diekman and her colleaguesElizabeth Brown, Amanda Johnston, EmilyClark, and Mia Steinberg addressed the potentialimpact of gender differences in “communal” and“agentic” values on the representation of womenin STEM fields such as engineering and computing.Psychologists use these two terms to describecontrasting fundamental orientations of humanexperience: Communal motivations describe anorientation to others and are associated with maintenanceof positive relationships; agentic motivationsare associated with self-advancement in socialhierarchies, making one’s own free choices, andacting independently (Trapnell & Paulhus, 2012).This chapter considers the degree to which theseconcepts help us understand women’s underrepresentationin engineering and computing.The motivation to help others is widely valued.In much of the world, including the United States,the majority of people—both women and men—endorse communal values, such as working for thewell-being of others, caring for the disadvantaged,and responding to the needs of others as their mostimportant guiding principles (Schwartz & Bardi,2001, as summarized in Grant, 2013). Individualswhose jobs allow them closer contact with the beneficiariesof their work report greater motivationand exhibit greater persistence in their jobs (Grant,2007; Grant et al., 2007), and people generally tendto positively evaluate those who are described ascommunally oriented (Diekman, 2007; Prentice &Carranza, 2002).Women and communalvaluesThe idea that women are more motivated than mento pursue work that helps people is a stereotype.The purpose of highlighting this research is notto perpetuate the stereotype but to explore thepossible ramifications of this small gender differencefor women’s representation in engineering andcomputing. Of course, not all women are motivatedto the same extent by the same goals (Quesenberry& Trauth, 2012), and certainly many men prioritizecommunal values more highly than many womendo. Nonetheless, on average, women are more likelythan men to say that they prefer work with a clearsocial purpose (Eccles, 2007; Lubinski & Benbow,2006; Pöhlmann, 2001; Konrad et al., 2000;Margolis, Fisher, & Miller, 2002). Even girls andyoung women are more likely than boys and youngmen to say that they want “helping others” to be animportant aspect of their future jobs ( Jones et al.,2000; Weisgram, Bigler et al., 2010).Working with andhelping peopleDiekman and her colleagues included two motivationswithin their definition of communal goalorientation: collaboration and helping (Diekman,Weisgram et al., 2015; Diekman & Steinberg, 2013).Although these concepts are separate and distinct,much of the evidence so far suggests that individuals’endorsements of these components of communalgoals tend to align with each other (Diekman,Weisgram et al., 2015). Diekman and her colleaguesare beginning to conduct research that teases apartthese two motivations to gain a more fine-grainedunderstanding of the influence of each element onvarious outcomes, but the latest research on thistopic combines the two motivations (Diekman &Steinberg, 2013).

AAUW 71How traditional gender rolesaffect valuesValues are not altogether freely chosen. Socialforces may teach girls and women to value helpingothers. Research shows that women are expectedto be nurturing and other-oriented and, unlikemen, are penalized when they don’t behave inaltruistic ways (Chen, J. J., 2008; Heilman & Chen,2005; Heilman, 2001; Heilman & Okimoto, 2007;Rudman & Glick, 2001; Rudman, Moss-Racusinet al., 2012). For men, who have traditionally occupiedprovider and leadership roles, self-orientedbehavior, such as earning money and gainingpower, are especially valued (Eagly & Wood, 2012;Eagly, 1987).Women’s and men’s prioritiesAs women have advanced in educational achievementsand labor force participation, they havebecome more similar to men in their motivationfor self-advancement. By some measures, womenare even more achievement-oriented than menare today. For example, in a recent survey of youngadults ages 18 to 34, women were more likely thanmen to say that being successful in a high-payingcareer or profession is very important or one of themost important things in their lives, representing agender reversal compared with responses to a similarsurvey conducted in the late 1990s (Patten &Parker, 2012). This example demonstrates the effectthat cultural factors have on individuals’ goals, sinceany biological factors related to motivation for selfadvancementcould not have changed much in theoverall population in the past two decades. Whilewomen and men have converged in how much theyvalue self-advancement, during the same period,gender differences in the value placed on workingwith and helping others have remained relativelystable and moderate in size (Eagly & Diekman,2003; Twenge, 1997).Perceptions of engineeringand computingEngineering and computing occupations are generallyperceived as unengaged with societal and communityconcerns and involving little contact withPerceived likelihood of goal fulfillment(scale from 1 to 7)FIGURE 20. PERCEIVED COMMUNAL ANDAGENTIC GOAL FULFILLMENT,BY TYPE OF CAREER76543210Engineering/computing/scienceSource: Diekman, Brown et al. (2010).Traditionallymale non-STEMCAREER TYPEAgentic goalsCommunal goalsTraditionallyfemaleother people (National Academy of Engineering,2008). In two studies Diekman and her colleagueslooked at how perceptions of these fields mightaffect women and men differently. In the first studythe researchers asked 360 undergraduate studentsto rate a combination of STEM, non-STEMtraditionally male careers, and traditionally femalecareers 1 by how much they would allow individualsto fulfill communal and agentic goals. Studentswere asked to estimate how well a career fulfilledagentic goals as well as how well it fulfilled communalgoals. 2 The findings were clear: Participantsrated engineering, computing, and science careersas less likely to fulfill communal goals than traditionallyfemale careers or traditionally male non-STEM careers (see figure 20).Career interestsParticipants then rated the goals according tohow important each goal was to them personallyon a scale ranging from 1 (not at all important)to 7 (extremely important) and rated their career

72SOLVING THE EQUATIONFIGURE 21. CAREER INTEREST AS A FUNCTION OFENDORSEMENT OF COMMUNAL GOALSCareer interest76543210Traditionallyfemale careersLowerTraditionally malenon-STEM careersHigherCOMMUNAL GOAL ENDORSEMENTNote: Scale from 1 (not at all) to 7 (extremely interested).Source: Diekman, Brown et al. (2010).Engineering/computing/scienceinterest in each of a number of STEM, traditionallymale non-STEM, and traditionally femalecareers. Diekman and her colleagues found that themore individuals endorsed communal goals, themore interest they expressed in traditionally femalecareers and the less interest they expressed in engineeringand computing careers. The trend shownin figure 21 is consistent with previous researchindicating that individuals who pursue engineeringand science careers on average place an unusuallylow value on having a job that directly benefitsother people or society (Eccles, 2007).Past achievement and confidenceIn the next part of the study, Diekman and hercolleagues assessed the participants’ belief in theirability to succeed (self-efficacy) in engineeringand computing careers and estimated participants’experience in STEM subjects, using enrollmentin STEM classes. The researchers found that evenamong students with strong past STEM achievementand belief in their mechanical, computational,and scientific abilities, those who were more motivatedto work with and help people were less likelyto express interest in engineering and computingfields. Communal goal endorsement predicteda lack of interest in engineering and computing.Finally, like other studies, this study (Diekman,Brown et al., 2010) found that women, on average,placed greater importance than men did onworking with and helping people, and a follow-upstudy (Diekman, Clark et al., 2011) confirmed thisfinding (see figure 22). 3Why relative assessment mattersResearchers theorize that an individual’s relative,rather than absolute, assessment of her or hisabilities and values is the critical factor in selectinga career path (Eccles, 2011b; Evans & Diekman,2009). Both women and men value communalgoals, but unlike men, women, on average, rankthese goals higher than self-advancement goals.Even though the gender differences in both thedesire for self-advancement and the desire to helpothers are small to moderate, they matter whenit comes to career choices. If individuals tend tochoose the career that best suits their abilities andbest matches their values, it follows that women, onaverage, would be more likely to enter into what areperceived as helping professions and less likely topursue engineering and computing careers.Diekman is quick to point out that genderdifferences in motivation to work with peopleand to help people do not fully explain women’sunderrepresentation in engineering and computingfields. When she and her colleagues controlledFigure 22. Average Goal Endorsement,by GenderWomenMenCommunal goals 5.6 5.0Agentic goals 5.1 5.3Note: Scale from 1 (not at all important) to 7 (very important).Source: Diekman, Clark et al. (2011).

AAUW 73for communal goal orientation, the gender differencein interest in a STEM career narrowed,but it did not disappear. In an interview withAAUW, Diekman said, “One of my fears is thatthis research will be interpreted as suggestingthat gender differences in communal goals are theonly thing that matters.” Without diminishingthe importance of other factors such as workplaceculture, gender bias, and stereotypes, Diekman’sresearch suggests that understanding individuals’motivation to work with and help people is animportant part of the puzzle of why women are lesslikely than men to pursue careers in engineeringand computing.The image of engineeringand computingIf the opportunities for achieving communal goalsin engineering and computing careers were bettercommunicated to the public, might more girlsand women aspire to these careers? Some indicatorssuggest that this might be true. For example,certain engineering disciplines with a clearer socialpurpose, such as biomedical engineering andenvironmental engineering, have had more successattracting a higher percentage of women than haveother disciplines, such as mechanical or electricalengineering (Yoder, 2013).Diekman, Clark, and their colleagues (2011)conducted a second study to test this hypothesismore broadly. In this study 241 students from anintroductory psychology course were asked to readabout a day in the life of an entry-level scientist.Participants were randomly assigned to read abouteither a scientist whose tasks involved working withand helping others or a scientist who completedthe same tasks without working with and helpingothers. Some gender differences emerged inparticipants’ responses. Women who read about thescientist who worked with and helped others weremore likely than other women to express a positiveattitude toward a science career. In contrast, amongmen no significant difference in positivity toward ascience career was found whether it was framed as acollaborative or an independent career.Thus, framing a science career as involvingmore collaboration and helping others resulted inwomen being more positive about that career withoutturning men away. Other research has foundthat, overall, students are more positive toward ascience career when they perceive it to be a careerthrough which they can help people (Weisgram &Diekman, 2014; Weisgram & Bigler, 2006).The reality of engineeringand computing careersDiekman’s second study suggests that showcasingcommunal aspects of engineering and computingcan change public perceptions. Engineeringand computing professionals have helped peopleand society in myriad ways, including improvingsanitation, fighting diseases, helping people withdisabilities, developing modes of transportationand infrastructure, connecting people around theworld, tracking the spread of diseases, and developingsustainable forms of energy. In addition, manyengineers and computing professionals work inteams.Yet, it is still important to consider whethermost engineering and computing jobs involveworking with and helping people. What if thestereotype that these are solitary jobs that don’tprovide opportunities for making a social contributionis mostly accurate? If that’s true, then recruitingcommunally oriented women and men intoengineering and computing through marketingcampaigns touting the collaborative, helpful aspectsof these fields will result in an exodus from the fieldlater. The reality, of course, is more important thanpublic perception.Some evidence suggests that engineering careers,in particular, fall short in providing opportunitiesfor fulfilling communal goals. One study of workingengineers found that women who expressed aninterest in social dimensions of work were morelikely than others to want to leave their engineeringjobs (Fouad et al., 2012). Fletcher (1999) foundthat female engineers frequently helped others,mentored others, and went the extra mile in theinterest of getting the job done, but this workwas frequently not recognized by others in the

74SOLVING THE EQUATIONengineering firm as “real” work. Rather, otherorientedbehaviors were viewed as outside the scopeof engineering work and misinterpreted as naturalexpressions of femininity. Most difficult for thefemale engineers, communal behaviors were bothexpected of female engineers and devalued by theengineering culture.Ironically, Diekman’s research suggests thatother-oriented activities (which are often viewed asseparate from and less valuable than the real workof engineering or computing) may be crucial forcreating and maintaining long-term engagementin engineering or computing work, especially forindividuals who are strongly motivated by communalgoals (Diekman, Weisgram et al., 2015).What can we do?Working with people and helping others are valuedgoals for most people (Schwartz & Bardi, 2001),especially women (Diekman, Clark et al., 2011).The more that engineering and computing educatorsand employers can incorporate communalgoals into their environment, the more open thedoors of these fields will be to people, many ofwhom are women, who strongly value working withand helping others.Employers, engineering and computing professionals,STEM teachers, parents, and anyoneseeking to motivate young people to considerengineering and computing careers can take somerelatively simple steps to put this research intopractice. Talk about the ways that engineers andcomputing professionals solve problems for society.Showcase how professionals’ everyday work alignswith the societally beneficial outcomes that arethe ultimate goals of engineering and technology.Individual engineers and computing professionalscan work through professional associations to mentorstudents and new co-workers. Employers canencourage and value efforts and activities that fulfillcommunal goals while at the same time providingvalue for their organization. One possibility is toformally recognize the necessary nontechnical work(such as working well with others and mentoring)as much as the necessary technical work in producinga product. Another possibility is to put in placepaid social service days where employees volunteerin their community.Chapter 6 notes1. STEM careers rated by participants included mechanical engineer, aerospace engineer, computer scientist, and environmentalscientist. Traditionally male non-STEM careers included lawyer, physician, architect, and dentist. Traditionally femalecareers included preschool or kindergarten teacher, registered nurse, social worker, human resources manager, and educationadministrator.2. Some of the agentic goals that Diekman and her colleagues included were power, recognition, achievement, individualism, success,demonstrating skill or competence, and competition with others. Some of the communal goals they included were helpingothers, serving humanitarian needs, serving the community, working with people, and connecting with others.3. In Diekman’s 2010 and 2011 studies, women endorsed communal goals more strongly than agentic goals and more strongly thanmen did. Unlike the 2011 study, however, the 2010 study found no statistically significant difference between men’s endorsementof communal goals and men’s endorsement of agentic goals: Men rated them equally important.

Chapter 7.College Environmentand CurriculumPeople often say it’s about the students at HarveyMudd. I don’t think it’s just about the students. I thinkit’s about the unified efforts of the faculty and thepresident. If a school wants to graduate more womenwith computer science degrees, it can’t necessarily makeits whole student body look like Harvey Mudd students,but it certainly can make its faculty work together.—Christine Alvarado

76SOLVING THE EQUATIONBecause many technical jobs require an engineeringor computer science degree, it is important tounderstand the effect of the engineering and computingcollege environment on women’s underrepresentationin those jobs. Many beginning collegestudents have little or no engineering or computingexperience, and the students who do have experienceoverwhelmingly tend to be men. Yet a numberof colleges and universities have succeeded in graduatinghigher than typical percentages of womenfrom their engineering and computing programs.Harvey Mudd College (HMC), a science- andengineering-focused liberal arts college with justunder 800 students in Claremont, California, iswidely recognized for dramatically increasing thepercentage (and number) of women majoring in itscomputer science program from a historical averageof 12 percent to approximately 40 percent over thepast five years (Alvarado & Judson, 2014) (see figure23). This percentage is well above the nationalaverage of 18 percent.Historically HMC, like almost all colleges anduniversities, had little success attracting women tocomputer science. In fact, until recently women atHMC were less well represented in computer sciencethan in any other field of study (Alvarado &Dodds, 2010); administrators and HMC professorsdecided to focus on increasing the representation ofwomen in the computer science program in 2006.Christine Alvarado, then a computer science professorat HMC (now a faculty member in computerscience at the University of California, San Diego),and her colleagues Ran Libeskind-Hadas, ZachDodds, and Geoff Kuenning based their efforts ona perception that, for the most part, students withoutprior computer science experience do not reallyunderstand what computer science is.Alvarado and her colleagues believed that thislack of understanding was an important reason fewwomen expressed interest in computer science. Theprofessors hypothesized that just by making clearto students what computer science is, they couldincrease the number of female computer sciencemajors and dramatically improve the computerscience experience for all their students. To achievethis goal, HMC implemented three major changesfocused on first-year students, well in advance ofwhen most students declare a major at the school(Alvarado, Dodds et al., 2012):1. HMC revised the introductory computer sciencecourse that presents the breadth of thecomputer science field in addition to the basicsof programming.2. HMC provided research opportunities forwomen immediately after their first year ofcollege to expose them to real computer scienceproblems as early as possible.3. HMC gave first-year students opportunities toattend the annual Grace Hopper Celebrationof Women in Computing conference hostedby the Anita Borg Institute for Women andTechnology.Computer sciencefor everyoneAll first-year students at HMC are required to takeand pass an introductory computer science class.Until 2005, everyone took the same Java-basedcourse, in which students were allowed to workahead to accommodate different levels of experience.Professors identified several problems duringthe course’s 10-year run. The class was too easy forsome and too difficult for others (Alvarado, Doddset al., 2012). Additionally, students “were comingout kind of feeling the same way they did whenthey came in,” said Alvarado in an interview withAAUW. She and her colleagues worried that thestudents weren’t developing an understanding ofwhat computer science was—or how “cool” it was.The students “weren’t seeing what we’re seeingabout this field,” she said. The professors determinedthat students were missing the true natureof computer science and its major contributions toother fields and society.These problems disproportionately affectedwomen because women, on average, come to HMCwith less computer science experience than men do(Alvarado & Judson, 2014). For this reason, womenwere apt to find the introductory computer scienceclass more difficult than their male colleaguesdid and were less likely to have a positive view ofcomputer science heading into the course. In addition,as described in chapter 6, women generally are

AAUW 77FIGURE 23. FEMALE COMPUTER SCIENCE GRADUATES NATIONALLY AND AT HARVEY MUDD COLLEGE,BY GRADUATION YEAR, 2000–2014605050%40Percentage3028% 28% 28%26%35% 35%38%2027% 25%22% 21%19% 18% 18% 18% 18% 18% 18%23%12%12%20%1010% 12% 6%13%14%3%02000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013YEAR OF GRADUATION2014Female computer science graduates nationallyFemale computer science graduates at Harvey Mudd CollegeSources: Harvey Mudd College Office of Institutional Research; AAUW analysis of data from National Science Foundation, Division of Science Resources Statistics (2013); L. M.Frehill analysis of data from National Science Foundation, National Center for Science and Engineering Statistics (2014a).more likely than men to express a preference forwork with a clear social purpose (Diekman, Clarket al., 2011; Lubinski & Benbow, 2006; Eccles,2007). Alvarado and her colleagues hypothesizedthat by highlighting the broad applications andsocial relevance of computer science they couldimprove the experience for all students and raisethe number of women in the major.In 2006 HMC replaced its introductory computerscience course with a course nicknamed “CSfor Scientists and Engineers,” which emphasizesthe breadth of the field first and uses Python, amore flexible and forgiving programming language,rather than Java (Alvarado, Dodds et al., 2012).HMC professors hoped to boost students’ enjoymentand increase the number of students whochose to continue in computer science by emphasizingits diverse practical applications. In addition,the professors hoped to create an enriching environmentfor students of all experience levels whilestill cultivating the programming and computationalthinking skills required for success in futurecomputer science courses (Dodds et al., 2008).Emphasizing practical applicationsand building confidenceThe revised introductory class covers the necessaryintroductory programming skills while emphasizingpractical computer science applications asdemonstrations of the importance of computerscience to other fields and to society. Alvarado toldAAUW that an overemphasis on programmingin an introductory computer science course canobscure the true nature of computer science:

78SOLVING THE EQUATIONWhat we try to emphasize at Harvey Muddis that computer science is not primarilyabout programming. You are not learningto program for the sake of programming.You are learning to solve problems usingprogramming as a tool … so you have tolearn how to use the tool effectively. We tryto emphasize the actual end goal and thehigher level concepts and then tie them backin and say, “Here’s how you use programmingto do that.”From the beginning of the course, students areexposed to the different ways in which computerscientists think about solving problems. Studentsare immediately assigned interesting programs towrite. The first assignment uses a language calledPicobot that controls a robot in a web simulation.Picobot is so easy to pick up that all students canwrite their own solutions to actual problems afterthe first class. At the same time it is a challenge foreveryone because none of the students have workedwith the language before (Alvarado, Dodds et al.,2012). Using a language that is new for everyonealso helps lessen the students’ perception that somestudents already know everything about computerprogramming while others know nothing. This perceptioncan be a demotivating factor for some students,especially women, in computing (Margolis &Fisher, 2002).Alvarado emphasized that the new courseaimed to encourage those with less experience. Shetold AAUW, “I think a huge part of what discouragespeople with no experience from pursuing computerscience in college is that they get into theseclasses, and they feel behind from the very secondthey sit in their seat.” She would like to see a shiftaway from the attitude that only those who canbe immediately identified as good programmersshould be encouraged to go on in computer science.Instead, she says, “If you give students the rightintroduction, the time that they need to blossom,and the environment where they feel comfortable,they’re definitely capable of doing the work, andthey’re interested in doing it.”To achieve this environment HMC developedtwo tracks for its revised introductory course.Students without computer science experience areplaced in the standard section (the Gold section)and those with prior computer science backgroundare placed in the enrichment section (the Blacksection). The two sections cover the same materialat the same time, but the Black section exploresmore challenging applications of the same fundamentalconcepts (Alvarado, Dodds et al., 2012):Topic and application distinctions are carefullychosen so that the additional material isenriching and motivating but that studentsin Gold are at neither a real nor a perceiveddisadvantage in CS2 [the computer sciencecourse that follows the introductory course]and subsequent classes. The goal of theBlack/Gold split is to give the inexperiencedstudents a comfortable and safe environmentin which to develop a passion for CSwithout interactions that might suggest—incorrectly—that they are too “far behind”other students to succeed in the field. Infact, the Black/Gold split has a less touted,but equally important, benefit: In the Blacksection we help debunk some of the misconceptionsthat experienced students bringto CS, for example, that mastery of syntacticconstructs is central to the field—or that CSis merely programming.“I have taught the Gold section for a couple ofyears,” Alvarado told AAUW, “and the first day ofclass I have asked students to raise their hands ifthey are nervous about the class. Probably about80 percent of the students raise their hands. Justlooking around and realizing they are surroundedby a bunch of people who don’t know what they aredoing either seems to make students feel better.”She continued:It is fun to teach the students who havegrown up knowing that they want to docomputer science—that’s great—but it iseven more exciting to teach the type ofstudents who have never really seen themselvesas computer scientists and never reallythought they could do it or have no experiencewith it and discover it. That’s how I gotinterested in women in computing—becausethere tend to be a lot more women in [thelatter] category.

AAUW 79HMC faculty carefully structured the revisedcourse to cover all the material required in an introductorycomputer science course in six fairly independentmodules, each representing a different waycomputer scientists think about problems. This formatallows students who might feel overwhelmedby a given topic to set aside the struggles of thatmodule before long and engage in a different set ofchallenges as the course develops. At the end of thesemester, professors build on the practical knowledgestudents have gained with a team project thatincorporates programming skills and encouragescreativity (Alvarado, Dodds et al., 2012).To assist the learning process, HMC introducedoptional weekly labs, staffed by faculty, forthe introductory course. As an incentive to participate,students who attend the weekly two-hourlab receive full credit for one of the three or fourweekly homework problems, regardless of whetherthey finish the problem or not. Inexperienced studentsbenefit from a lightened workload, and morecontact with faculty encourages students to get helpearly with difficult concepts (Alvarado & Dodds,2010).Results of the redesignThe results have been overwhelmingly positive. In2007, one year after the revised course was initiallyoffered, Alvarado and her colleagues compared thelater performance of students who had taken theoriginal introductory computer science course withthose who had taken the revised course. At thattime, some of the students taking CS2 had takenthe revised course in 2006, and others had takenthe original introductory course. Although CS2involves significant Java programming, which wastaught in the original introductory course but notin the revised course, and does not use Python, theprogramming language used in the new introductorycourse, midterm exam scores for students whohad taken the revised course averaged 84 percentcompared with 80 percent for students who hadtaken the original course. Likewise, students whohad taken the revised course averaged 85 percenton the CS2 final exam compared with an average of80 percent for students who had taken the originalcourse (Dodds et al., 2008). Equally important, asurvey found that 75 percent of students who tookthe revised course felt that it changed their perceptionof computer science, whereas only 47 percentof students who took the original course felt thatway (Alvarado & Dodds, 2010).Promoting early researchexperiencesThe second major change that Harvey MuddCollege introduced was the opportunity for womento participate in computer science research withina year of starting college. The goal of this changewas to instill in young women the confidence thatthey can do real-world computer science. Whilephysical sciences often provide first- or second-yearstudents with the opportunity to participate in labresearch in a supporting role, until 2006 computerscience research opportunities at HMC weremostly available to students who had completedseveral computer science courses. Beginning in2006, HMC began to offer research experiences towomen the summer after their first year of college,before most had declared a major. Despite havingcompleted only one or two introductory computerscience courses, these students make concrete progresson real research problems each year (Alvarado& Dodds, 2010).Accumulating evidence suggests that researchexperiences have a notable effect on encouragingwomen to pursue computer science. Amongthe women who participated in computer scienceresearch between 2007 and 2011, 66 percent choseto major in computer science, compared withless than 20 percent of HMC students overall(Alvarado, Dodds et al., 2012). Evidence showsthat these research experiences have a bigger influenceon women than on men. Looking only atstudents who participated in summer computer scienceresearch after their first year, the survey foundthat 67 percent of female students compared with25 percent of male students listed research experienceas an influence in choosing a computer sciencemajor (Alvarado & Dodds, 2010). While thenumbers of students participating in these researchopportunities are still fairly small, the results suggestthat the opportunity to conduct computer

80SOLVING THE EQUATIONscience research early in women’s college careerscan provide important encouragement for them topursue computer science.The Grace HopperCelebration of Womenin ComputingThe third major change that Harvey Muddinstituted was providing first-year students withthe opportunity to attend—for free—the GraceHopper Celebration of Women in Computing(GHC), a conference held each fall since 1994.Named after Rear Admiral Grace Hopper, an earlycomputing pioneer who created the first compilerin 1952, GHC has grown into a gathering ofthousands that provides support and communityfor women in the field of computing and includesmany students. Results from the 2013 Evaluationand Impact Report (Anita Borg Institute, 2014b)indicate that students feel less isolated, morecommitted to computing, and more inspired afterattending the conference. In 2013, 85 percent ofthe students who responded to the survey agreedthat attending GHC increased their commitmentto a technology career.HMC began taking first-year female studentsto GHC in 2006. A primary objective is to recruitand retain first-year women into computing bycombating some of the factors that have beenshown to prevent women from pursuing computingcareers, including a lack of confidence, a perceptionof the culture as geeky or hostile, a misunderstandingof the field, and a lack of mentors and supportnetworks. At GHC, students see a computing culturethat is different from the stereotypical culturethey might expect. They are exposed to real-worldcomputing applications, interact with computingprofessionals, learn first-hand about excitingcomputing-related companies, and increase theirnetwork of friends and colleagues. Twelve studentsfrom HMC attended the conference in 2006, andthe number has grown every year to 52 students in2014. The computer science department recruitsstudents by e-mail during the summer before theirfreshman year and takes students regardless ofwhether they have expressed an interest in computerscience.Results indicate that attending GHC at a criticaljuncture in a student’s decision process (three to18 months before declaring a major) can dramaticallyaffect a student’s major and likely career path(Alvarado & Judson, 2014). Starting in 2007 andmore formally in 2009, HMC has conducted anannual survey of conference attendees to try tounderstand the effect the trip has on students.Survey results from 2009 and 2010 indicate thatthe conference is effective in addressing the barriersthat keep women from choosing to studycomputer science. Eighty-eight percent of studentsresponded that attending GHC gave them a betterunderstanding of computer science, 85 percent saidGHC changed their perception of the computerscience culture, 72 percent indicated that GHCincreased their desire to take another computer scienceclass, and 62 percent said that GHC increasedtheir desire to major in computer science.The conference has an effect on students withsome computer science background as well asthose without. Nearly four out of five students (78percent) who were already considering majoringin computer science agreed that attending GHCincreased their desire to major in computer science.Among GHC attendees who were not consideringmajoring in computer science before the conference,a majority (64 percent) indicated that attendingthe conference increased their desire to takeanother computer science course, and 43 percentsaid that attending GHC increased their desire tomajor in computer science (Alvarado & Judson,2014).By examining enrollment patterns, Alvaradoand her colleagues found that students whoattended GHC were indeed much more likely totake another computer science course (52 percent)and major in computer science (37 percent) thanwere female students who did not attend (31 percentand 10 percent, respectively). Certainly someof this effect is due to self-selection, since thosemore interested in computer science probably moreoften applied to go to the conference. Yet lookingonly at those who came to HMC with no intentionto major in computer science, 25 percent of those

AAUW 81Figure 24. factors Contributing to HarveyMudd College Students’ Choice to Majorin Computer Science, by GenderTaking the revised introductorycomputer science courseParticipating in earlyresearch opportunitieswho attended GHC went on to major in computerscience whereas only 10 percent of those who didnot attend GHC went on to major in computerscience. This point bears repeating: One of fourwomen who came to Harvey Mudd not consideringa computer science major and who attendedGHC ended up majoring in computer science.An HMC survey found that all three changesthe school instituted to increase the number ofwomen computer science majors did in fact do so,particularly the revisions made to the introductorycourse (see figure 24).Beyond Harvey MuddWomen 85%Men 81%Women 67%Men 25%Attending the Grace Hopper Celebration Women 47%Note: Percentages indicate survey respondents who participated in the practice whoreported that it contributed to their choice of a computer science major.Source: Alvarado, Dodds et al. (2012).Harvey Mudd’s success story is important andencouraging for other colleges and universitiesstruggling to diversify their computer sciencedepartments, but how transferable are the changes?Some school administrators might believe that thissuccess was possible only because of HMC’s smallsize and science and engineering focus. Scalabilityand logistics are major concerns for larger and morediverse colleges and universities looking to makesimilar changes.Now at the University of California, San Diego,Alvarado has first-hand experience attemptingto institute similar changes at a larger, morediverse institution. She identified the much higherstudent-to-faculty ratio at UCSD as one of the bigchallenges: “It’s more difficult to track students,it’s more difficult to schedule students, it’s moredifficult to get messaging out to students, it’s moredifficult to coordinate across these huge sectionsand across multiple offerings of the same course—the logistics are difficult.” Nevertheless, a small butgrowing body of evidence suggests that the changesare transferable with some modifications to fitspecific departments.Institutional commitmentTo begin with, Alvarado emphasizes the need foran institution-wide commitment to addressingwomen’s underrepresentation:The most important thing we had going forus at Harvey Mudd was that we had widesupport throughout the department andthroughout the college. We had our president,Maria Klawe, pushing for this, and shewas essential in making this work. She got inand started talking it up to people, and shefound us money and personal connections.She provided support at a very high leveland made people pay attention. I think wecan talk about the individual things we didat Harvey Mudd, and those are important,but until [increasing the representation ofwomen in computing] is made a priority ata school in a very broad way, these initiativesmay not be successful.expanding on the HMC modelHarvey Mudd’s documented progress has recentlyprompted several companies, including Facebook,Intel, Google, and Microsoft, to give more than $1million to 15 colleges and universities to institutesimilar changes. As part of the Building Recruitingand Inclusion for Diversity initiative led by the AnitaBorg Institute for Women and Technology and HMC,researchers will gather data on participating institutions’progress in increasing women’s representationin computing to better understand whatkinds of changes work in which contexts (AnitaBorg Institute, 2014a).

82SOLVING THE EQUATIONRevising the introductorycomputer science courseVariations of Harvey Mudd’s revised introductorycomputer science course have now been taught at anumber of other colleges and universities, yieldinginsights into how well it can be adapted to otherenvironments. Bucknell University adopted theGold approach in fall 2011, with high levels ofsatisfaction reported among faculty and studentsdespite an increased workload and many studentsreporting that the course influenced their decisionto major in computer science (Alvarado, Doddset al., 2012). At the University of California,Riverside, the introductory course was not dividedinto sections according to the background of thestudents. Professors found that this negativelyaffected the experience for some students. Fromthis result Alvarado and her colleagues determinedthat maintaining the separation of students withdifferent backgrounds in computer science is anintegral part of the success of Harvey Mudd’sapproach, perhaps particularly at large, diverseinstitutions.If a full redesign is impossible, Alvarado and hercolleagues suggest that small changes to existingcourses can make a big difference. For example,teaching computer science in a contextualizedway that gives students an opportunity to see itsapplications is increasingly common at colleges anduniversities across the country. Alvarado points toGeorgia Tech, where professors developed a courseto teach computer science concepts by manipulatingdigital media, such as pictures and sounds,as a good example of a contextualized computingcurriculum with more standard introductorycontent. While science-heavy applications thatwork at HMC may not work everywhere, almostall disciplines include computation in some form.Computer science professors can construct meaningfuland relevant assignments from any numberof fields (Alvarado, Dodds et al., 2012).Early research experiencesResearch opportunities are the part of the programthat Alvarado is finding the easiest to transferto UCSD: “We have a very active set of researchprojects here at UCSD, and it’s relatively easy justto plug into the research that is going on already.”Guiding undergraduate research projects for studentswith little computer science experience mayseem daunting for faculty. Yet Alvarado and hercolleagues suggest that with the right structures inplace, it is quite doable. At Harvey Mudd, incorporatingseveral elements has led to significant satisfactionamong both students and faculty (Alvarado,Dodds et al., 2012):• Open-ended projects with sufficient scaffolding.Each project should include a well-definedsequence of tasks to accomplish, along with asubstantial creative component.• Teamwork. Students benefit from workingtogether. Faculty advisers must ensure thatmentor-partner relationships are natural andcomplementary and that teams work welltogether.• Lots of communication. Daily communication—ideallytwice a day—with faculty advisersis critical. Insist that participants maintain acareful daily log of their activities and questionsto make meetings with faculty more efficient.Attending the Grace HopperCelebrationHow feasible is it for other schools to send theirfemale computing students to Grace Hopper?According to Alvarado, the first question peopleoften ask is about funding. HMC has been pleasantlysurprised at its ability to raise internal andexternal funding to send all students who haveapplied to GHC because of a high interest frompotential employers, alumni, and philanthropists.Demonstrated results should assist in further fundraisingfor HMC and other schools (Alvarado &Judson, 2014).Even if colleges and universities can raise fundsto send students to GHC, size limitations of GHCcould also be an issue. While GHC continues togrow, it also sells out every year. One answer to thisproblem is more celebrations. Regional conferences,hosted by a partnership of the Anita Borg Institutefor Women and Technology, the Association forComputing Machinery Women’s Council, andthe National Center for Women and Information

AAUW 83Technology, are springing up around the UnitedStates and around the world. Alvarado has focusedon the regional Grace Hopper Celebrations forthe students at UCSD because the conferences areeasier (and less expensive) to reach and are oftenfree for students. Alvarado said:Regional celebrations can be better becausethey are not as big and intimidating. Eightthousand people attended the most recentnational Grace Hopper Celebration, andfirst-year students can feel kind of lost.Regional celebrations are much moreintimate—around 200 people—and for themost part mainly students attend.What can we do?Since 2006, when it first implemented the practicessummarized above, Harvey Mudd College has seena dramatic and lasting increase in the number andpercentage of women who choose to major in computerscience. Clearly the turning point in women’srepresentation occurred with the class of 2010 (seefigure 23), the first class that experienced the newpractices (Alvarado, Dodds et al., 2012).HMC hasn’t solved all the problems, however.When students who had considered majoringin computer science but decided against it wereasked to list important factors in their decision, 28percent of women and 31 percent of men listed“I didn’t feel like I fit in as a CS major/I didn’tfeel comfortable with the culture” as an importantfactor. In addition Alvarado and her colleaguesfound that more women (26 percent) than men(14 percent) chose a different major in part becausethey thought that computer science was too hard(Alvarado & Dodds, 2010).Still, HMC’s experiment is encouraging, notleast because the school has also seen a significantincrease in the number of men majoring incomputer science in the past few years. From dataHMC has collected, it appears that as many menas women enjoy and are motivated by the revisedintroductory course. When HMC asked thegraduating classes from 2009 to 2012, “What is theSINGLE most important experience that led youto choose a CS major?” the revised course was themost cited experience for women (34 percent) andtied for the most cited experience for men (29 percent)with “experiences before college” (Alvarado,Dodds et al., 2012).Recommendations that stem from HarveyMudd’s success include exposing a broad range ofpeople to computing and moving away from theidea that certain people (often with strong programmingskills) are cut out for computer sciencewhile others are not. High schools, middle schools,and elementary schools should offer courses incomputer science. The more experiences studentshave with computer science before they get to college,the more opportunities will be open to them.Colleges and universities should require allundergraduate students to take at least one computerscience course, no matter what their major,and should engage students in hands-on researchearly in their college education. College and universitycomputer science departments should highlightas early as possible the different facets that makeup computer science and show the impact thatcomputer science has on society.Departments should split classes by experience,providing students with less experience in computerscience with the time and environment theyneed to build their skills and interest. Even withoutchanging an introductory computer science course,it may be possible to develop at very little costmultiple sections to accommodate students withdifferent experience levels.Computer science departments should take studentsto the Grace Hopper Celebration of Womenin Computing or similar conferences to give them asense of identity with people in the field and sharetheir excitement for it. Taking even a few studentscan change their mindset and have an importanteffect on a school’s program. Schools should take amix of students interested in computing and thosenot considering computer science as a major.

Chapter 8.Persistence andSense of FitWomen, after a few weeks or months or years inengineering, might begin to see themselves as lesscapable professionals than men because they areinteracting in a space that tends to be masculine andtends to devalue what are seen as feminine traits orfeminine contributions to the field.—Erin Cech

86SOLVING THE EQUATION“Being an engineer” includes more than knowledgeand skills in mathematics and science. Prospectiveengineers must be able to deal with ambiguous,messy problems, and they need to share a commitmentto the values of the profession and becomfortable with its norms. Erin Cech, an assistantprofessor of sociology at Rice University, andher colleagues Brian Rubineau, Susan Silbey, andCarroll Seron noticed that not much researchaddressed “not only the confidence a person hasin his/her ability to do things like take a math testor take a science test but also the confidence toimagine oneself going out and applying for a jobin engineering and being an engineer.” They coinedthe term “professional role confidence” to describehow comfortable professionals or prospective professionalsare in their ability to fit into and fulfill allthe aspects of their roles. Cech and her colleaguesmeasured the influence of this sense of fit on thepersistence of women and men in engineeringprograms in colleges and universities.Expertise and careerfitconfidence amongundergraduatesIn engineering, a field in which the educationaland professional environments are closely linked,professional role confidence starts to develop aswomen and men begin their engineering education.Undergraduate engineering students are engineersin-training,adding direct experiences with the professionto their previous understanding of the field.As they train for their careers, engineering studentsconsider whether they will be successful, happyprofessionals in their chosen field. Whether or notthey see themselves as successful and fulfilled andwhether they feel that engineering is a good fit fortheir skills and interests will affect whether theycontinue working toward a degree and whetherthey pursue an engineering career.Cech and her colleagues (2011) further refinedthe concept of professional role confidence bydividing it into two discrete concepts: expertiseconfidence (the confidence that one possessesthe requisite skills and knowledge to be a professionalin a chosen field) and career-fit confidence(confidence that the field is consistent with one’sinterests, values, and identity). Expertise confidenceanswers the question, “Do I have the capabilitiesto be successful in this role?” Career-fit confidenceanswers the question, “Is this the right career forme long term?”To understand gender differences in professionalrole confidence and the impact of professionalrole confidence on retention in engineering,Cech and her colleagues interviewed 288 engineeringstudents in the second semester of their firstyear and again in their fourth year of college. Thestudents studied at four universities: a land-grantcollege typical of the public institutions that educate80 percent of U.S. engineers, a highly rankedprivate engineering school, and two small, innovativeprograms developed to challenge the engineeringeducation offered at conventional engineeringschools.During their first semester, students at all fourschools were required to take a course intended tointroduce them to the engineering profession. Asdescribed by Cech (2014, p. 49), students quicklydevelop a sense of the culture and norms once theyenter their training program:Through classes, internships, design projects,and friendships, students are transformedfrom laypersons into engineers; they areexpected to adopt the profession’s epistemologies,values, and norms; identify withparticular symbols; and learn to project aconfident, capable image of expertise.Cech and her colleagues assessed first-yearengineering students’ expertise confidence by askingthem to rate their confidence on the followingthree indicators as a result of their engineeringcourses:• Developing useful skills• Advancing to the next level in engineering• Believing that I have the ability to be successfulin my careerThe researchers assessed career-fit confidenceby asking students in their first year of engineeringschool to rate their confidence in the following fourindicators as a result of their engineering courses:

AAUW 87Measuring professionalrole confidence earlyAssessing students’ professional role confidenceduring their first year of engineering school mayseem early. How confident can young people feelabout their engineering expertise and about howwell an engineering career is likely to fit them lessthan a year into their training? In an interview withAAUW, Cech explained why it makes sense to assessprofessional role confidence so soon:Students come in with a fairly vagueunderstanding of what this occupationalfield is. They often get information aboutthe field from websites or friends or relativeswho are engineers. It’s not until theystart taking classes in the major and startmeeting other members of their major thatthey start their professional socializationas engineers. And the socialization processis quite intense. The undergraduate experienceis not only about learning how to dothe homework assignments but learningwhat it means to be an engineer. So eventhough we take this measure in the springsemester of the freshman year, they’vealready encountered a rather intensesocialization process where they’re tryingto figure out—talking with their classmates,talking with professors—what itmeans to be part of this profession andwhether it is the right fit for them.• Believing that engineering is the right professionfor me• Selecting the right field of engineering for me• Finding a satisfying job• Feeling a commitment to engineeringFor their persistence measures, Cech and hercolleagues assessed two kinds of persistence: behavioralpersistence (students’ actual completion ofan engineering major between the first and fourthyears) and intentional persistence (students’ beliefin the fourth year that they will be an engineer infive years).Staying with the programCech and her colleagues (2011) found that menexpressed more behavioral and intentional persistencethan women did. That is, men were morelikely than women to persist from the first to thefourth year and actually earn an engineering degree,and in their senior year men were more likely thanwomen to report that they intended to be an engineerin five years.In addition, men expressed significantly moreprofessional role confidence, both expertise confidenceand career-fit confidence, than women did,even after controlling for students’ actual performance.Consistent with the researchers’ hypothesis,students with greater confidence in their expertiseand career fit were more likely to persist in engineering.Most important, when women and menhad the same expertise confidence and career-fitconfidence, women were no less likely to persistthan men—either in completing an engineeringdegree (behavioral persistence) or in intendingto work as an engineer in the future (intentionalpersistence). In other words, women and men withsimilar views on whether engineering was a goodfit for their skills, interests, and values were equallylikely to continue in engineering in school andintend to continue in engineering in the workforce.The study also assessed the influence of familyplans and self-assessment of mathematical abilitybut did not find evidence that either of these measureslowered women’s persistence in engineeringduring engineering school.Looking at the different measures of persistence,Cech and her colleagues found that expertiseconfidence predicted behavioral persistence andthat career-fit confidence predicted intentional persistence.In other words, students who believed thattheir skills and expertise fit the engineering fieldwere more likely to persist in the major and earnan engineering degree. On the other hand, studentswho felt that their interests and values fit the

88SOLVING THE EQUATIONculture of engineering were more likely to intend tobe an engineer five years after graduation.The finding that expertise confidence has littleimpact on plans for a future career in engineeringwhile career-fit confidence is strongly and positivelyrelated to future career plans is particularlyimportant. The degree to which students expect tofit in with the engineering culture and norms is astrong indication of their intention to pursue anengineering career, much more so than students’confidence in their engineering skills.A gendered sense of fitNaturally, engineering students expect that theskills taught in engineering courses will be theskills they will use in the field. But Cech arguesthat engineering degree programs largely ignore avariety of areas of expertise that are necessary to besuccessful as a professional engineer:The classes that students take are overwhelminglymath- and science-based, withperhaps one class on technical writingand one class on engineering ethics. But ifyou talk to engineers who are in the field,they will tell you they are required to havea whole fleet of other kinds of skills to besuccessful. They need to be good communicators,they need to be good managers,they need to be organized, and they need tounderstand the complexities of the relationshipbetween the technical things they’reworking on and other social processes.A narrow math and science emphasis disproportionatelydisadvantages women because itemphasizes male-stereotyped skills while devaluingskills that are gender neutral or female-stereotyped,such as writing, communication, and managerialskills. Cech recommends displaying the need forthese other competencies by providing opportunitiesfor undergraduates to do actual engineeringand design work as soon as possible. Working onengineering problems in the field, Cech says, differsfrom solving homework problems, and she wouldlike to see undergraduates “have exposure to whatthe profession of engineering actually is, ratherthan what they imagine the engineering professionto be.” Engineering students may be developinga sense that the engineering profession is not theright fit for them based on mistaken or incompleteperceptions of what the work is actually like.As described in chapter 6, a second reason thatthe undergraduate engineering experience may beturning women away from engineering is a lackof emphasis on the social impact of engineeringwork. Cech (2014) found that students’ interest inpublic welfare concerns declined over the course ofthe undergraduate engineering program. She toldAAUW:The professional socialization that [studentsare] getting in these engineering programsis generally not emphasizing things likepublic welfare issues and social consciousness.This relates to the broader issue of howwe represent the engineering profession atits fullest level of complexity, not characterizingit as only math and science equationsbut articulating the variety of things it hasto offer. Portraying engineering in this morecomplex way would allow a broader spectrumof students to believe that engineeringis the right field for them, to have greatercareer-fit confidence and greater expertiseconfidence. More students would likely seeengineering as something that they could begood at. There are very deeply rooted needsfor consideration of social justice and publicwelfare concerns in the kind of work thatengineers do, and separating these kinds ofquestions from the standard curriculum isproblematic.Fitting in at workCech’s study specifically measures the professionalrole confidence of undergraduates. Yet from allappearances, the issue of women leaving the engineeringworkforce is more significant than the issueof women leaving college engineering programs.Cech told AAUW:There is nothing to suggest that these processesend after people walk across the stageand have their degree. We don’t leave ourprofessional training and go into the workforceand never again worry about how we’re

AAUW 89doing and whether or not we have the rightexpertise or whether the profession is theright fit. These are questions that continuallycome up for young professionals and perhapsprofessionals throughout their career.Cech and her colleagues are following up withthe students who participated in the study to seehow professional role confidence in college relatesto persistence in the engineering workforce.Research on the influence of professional roleconfidence in the engineering field is in its earlystages. The research to date demonstrates thatprofessional role confidence is significantly associatedwith engineering persistence and that womentend to have less professional confidence than menhave. Development of an identity as an engineerhas been shown to be a characteristic of womenwho persist in engineering careers (Buse et al.,2013). These findings point to increasing women’ssense of fit with the professional role as a potentialkey to closing gender gaps in engineering. Whenasked what can be done to increase professionalrole confidence among women in engineering,Cech acknowledged that it is difficult: “It requireschanging the culture of the field, the culture ofthe profession.” Increasing the number of womenwho are comfortable being an engineer likely willrequire sustained effort from engineering schoolsand workplaces to make clear the reasons whywomen belong in engineering.What can we do?A number of recommendations stem from thisresearch. College and university engineeringdepartments should emphasize the wide variety ofexpertise necessary to be successful as an engineer.A narrow focus on math and science obscures theother areas of expertise—writing, communicating,organizing, and managing—that engineers needto be successful. Including engineering designactivities in the field early in undergraduate courseworkallows students to see the differences betweentextbook problems and the creativity and criticalthinking necessary for actual engineering problemsolving. Recognizing that these areas of expertiseare critical to the engineering role also shifts theimage of who is a good fit for engineering andstops the devaluing of competencies and contributionsthat are female stereotyped.Enable early contact between students andprofessionals. Meaningful contact with engineersin the field provides students with role models andmentors and also helps students understand thebreadth of skills that they will need to be successful.Individuals with low professional role confidencecould benefit from interaction with professionalswith whom they can identify.Provide girls with opportunities to tinker. Moreboys than girls arrive at college with experiencetinkering or programming. Giving girls opportunitiesto develop confidence in their design abilitieswell before they finish high school could help themdevelop expertise confidence and comfort withbeing an engineer, which could then increase theirpersistence in the field. Increasing the number ofgirls who enter college with experience in designcould also help shift the perception that women arenot naturally a good fit for engineering work.Finally, spread the word that engineering skillsand competencies are learned, not innate (Dweck,2007). A conception that some people’s brains arehardwired to do engineering work (and that menare better at math and science than women are)contributes to low professional role confidence byperpetuating a stereotype that some people arenatural engineers while others are a poor fit forengineering. In engineering classrooms, reinforcingthe idea that successful engineers are those willingto practice to develop their skills and persistthrough difficulties can help reduce false assumptionsabout engineering competence.

Chapter 9.A Workplacefor EveryoneA lot of the studies have focused on fixing women—fixingtheir confidence, fixing their interests. We did not findthat any of those factors influenced women engineers’persistence decisions at all, which is why we are sayingwe really need to be focusing on the environment.—Nadya Fouad

92SOLVING THE EQUATIONMany researchers have looked at why womenchoose to enter STEM careers such as engineeringand computing and the factors involved inpreparing women for these careers, but retention ofwomen in these fields has received much less attention.Recruiting women will be truly successful onlyif women who start in engineering and computingstay in these fields. Despite evidence that womenare more likely than men to leave engineering andtechnical jobs (Hunt, 2010; Society of WomenEngineers, 2006; Hewlett, Buck Luce et al., 2008),few researchers have studied why, and under whatconditions, women choose to leave.A survey of womenengineersIn 2009, distinguished professor of educational psychologyNadya Fouad and business school professorRomila Singh at the University of Wisconsin,Milwaukee, along with colleagues Mary Fitzpatrickand Jane Liu, launched a survey funded by theNational Science Foundation to uncover the factorsthat lead women to stay in engineering. Drawingfrom the population of women who had graduatedwith an undergraduate degree in engineering atany time in the past (Fouad et al., 2012), the surveycompared women currently working as engineerswith women who had left the field. Participantsincluded women who earned engineering degreesas far back as 1947 and as recently as 2010.The researchers were determined to reach asrepresentative a sample as possible, so they contactedengineering deans at the top 50 universitiesthat graduate women in engineering. To understandissues particular to women of color in engineering,the researchers also contacted the top 20universities that graduate Latino, black, and Asianengineers. Thirty universities agreed to participate,including universities in every region of the UnitedStates: private institutions such as Cornell andStanford; public universities such as Penn State andthe University of Florida; and technical schoolssuch as MIT and Georgia Tech.In addition to the women at the 30 official partneruniversities, women from another 200 universitiescompleted the survey (at NSFpower.org) afterhearing about it from others (Singh et al., 2013). Inthe end, the researchers received more than 5,500responses.The survey was designed to assess factors thatcould influence engineers’ likelihood of leaving thefield, such as vocational interests, job and careersatisfaction, work-family conflict, training anddevelopment opportunities, a variety of workplacesupport mechanisms and initiatives, underminingbehavior in the work environment, and surveytakers’ commitment to their employer and theengineering profession. The survey also includedquestions that allowed the researchers to assessrespondents’ self-efficacy (their belief or confidencein their ability to complete tasks or reach goals) andpositive outcome expectations (their belief or confidencethat a given behavior will lead to a particularoutcome).Fouad, Singh, and their colleagues publisheda report in 2012 based on the more than 5,500survey responses from women with engineeringdegrees. 1 More than half of the respondents werecurrently working as engineers, about a quarter hadpreviously worked as engineers but had left thefield, and the rest had earned engineering degreesbut never worked as engineers.Those who stayed versus thosewho leftTo understand any possible differences betweenwomen who had left and women who had stayedin engineering, the researchers compared thesurvey responses of women currently working inengineering with the responses of women who hadleft within the previous five years. 2 By a surprisingnumber of measures, the two groups of womenwere similar: They had similar undergraduatemajors, were equally likely to be married and havechildren, were of similar racial/ethnic backgrounds,and were of similar age.The two groups of women were also psychologicallysimilar, being equally interested in engineeringand equally confident in their engineering abilities.Both groups were equally confident in their abilityto navigate the political environment in their workplaceand their ability to manage multiple work-life

AAUW 93role demands and had similar expectations in thesearenas.What was not the same for the two groupsof engineers was the workplace environment.Compared with those who stayed in engineering,those who left were• Less likely to report opportunities for trainingand development that would have helped themadvance• Less likely to report support from a supervisoror co-worker• More likely to report undermining behaviorsfrom supervisors• Less likely to report support for balancing workand nonwork rolesIndeed, the survey clearly showed that womenare not leaving because of something they arelacking. As Fouad told AAUW, “A lot of thestudies have focused on fixing women—fixingtheir confidence, fixing their interests. We did notfind that any of those factors influenced womenengineers’ persistence decisions at all, which is whywe are saying we really need to be focusing on theenvironment.”Workplace barriersComparing persisters with nonpersisters is usefulfor understanding which factors contribute towomen leaving engineering. To better understandthe differences between women who are solidlycommitted to their engineering jobs and those whomay leave, the researchers analyzed how currentengineers’ job satisfaction and intention to leavetheir organizations related to a number of otherfactors. They found two workplace factors thatlowered job satisfaction: excessive and ill-definedwork goals and various kinds of incivility, includingexplicit insults directed at women.Excessive workloads included being expectedto work more than 50 hours per week and takework home at night and on weekends, as well astoo much responsibility without commensurateresources such as budget, staff, and time. Illdefinedresponsibilities included a lack of clearlydefined goals, objectives, and responsibilities andcontradictory and conflicting work requests andrequirements. Engineers burdened with excessiveand ill-defined work goals were the least satisfiedwith their jobs and the most inclined to leave theirorganizations. Overwork and lack of clarity aboutassignments were related not only to a weakercommitment to their specific employer but to theengineering profession as a whole.Incivility at workThe second factor that lowered women engineers’job satisfaction, incivility at work, included beingtreated in a condescending, patronizing, or discourteousmanner by supervisors, senior managers,and co-workers. The researchers asked participantsto estimate on a scale of 1 (never) to 6 (every day)how frequently in the past month they had experiencedspecific undermining behaviors (from supervisorsand co-workers separately), such as havingtheir ideas belittled; being insulted, talked downto, not defended, or given the silent treatment; andgenerally having their efforts to be successful onthe job undercut. In addition, participants wereasked how often in the past year they had observedany supervisor, senior manager, or colleague exhibitsexist behaviors such as ignoring or interrupting afemale employee, speaking in a condescending orpatronizing manner to a female employee, makingoffensive or embarrassing public comments aboutthe physical appearance of a female employee, ormaking sexually suggestive comments to a femaleemployee.Figure 25 shows that, on average, engineerswho reported less satisfaction with their jobs alsoreported observing more sexist behavior at work inthe last year and experiencing more underminingbehaviors by both co-workers and supervisors inthe last month. While the differences are not huge,they are statistically significant, and, of course, noone should have to endure these types of behaviorsin the workplace or elsewhere, so the answer to allthree of these questions should be never.Female engineers who reported that supervisorsmore frequently belittled, patronized, or systematicallyundermined them were the least satisfied withtheir jobs and were less satisfied than those receivinguncivil treatment from co-workers. Womenin such undermining environments were also less

94SOLVING THE EQUATIONMean frequencyFIGURE 25. FEMALE ENGINEERS' EXPERIENCEOF INCIVILITY AT WORK, BY LEVEL OFJOB SATISFACTION2.01.81.61.41.21.0Observed sexistbehaviorExperiencedunderminingbehaviorsby supervisorEngineers withlow job satisfactionEngineers withhigh job satisfactionExperiencedunderminingbehaviorsby co-workersNote: Scale from 1 (never) to 6 (every day). Participants estimated the frequency withwhich they experienced undermining behaviors in the past month and the frequencywith which they observed sexist behavior in the past year.Source: Fouad et al. (2012). AAUW communication with Romila Singh.committed to the organization as a whole, morelikely to intend to leave their job, and more likely toexperience high levels of work-family conflict.These findings are particularly revealing because,although some research has identified the engineeringwork culture as unfriendly to women, this studyis the first to identify specific kinds of underminingbehaviors that may contribute to an uncomfortablework climate for women and affect their inclinationto leave the field of engineering.Why women stay in engineeringAccording to Fouad, Singh, and their colleagues,current engineers who were the most satisfied withtheir jobs and committed to the field of engineeringoverall were the most likely to experiencesupport in the workplace. Engineers who weresatisfied with their jobs felt that their contributionswere valued and recognized and that theirorganization cared about their well-being, opinions,and general satisfaction at work. They also receivedmore tangible development opportunities, such aschallenging assignments that helped them advancetheir skills and formal training opportunities.They worked for organizations that provided clear,transparent paths for advancement and were morelikely than engineers with low job satisfaction toreport that their organization provided supportivework-life policies.Self-efficacy and positiveoutcome expectationsDelving a bit deeper, Singh, Fouad, and theircolleagues Mary Fitzpatrick, Jane Liu, KevinCappaert, and Catia Figuereido conducted afollow-up analysis (Singh et al., 2013) of twofactors that they suspected might be especiallyimportant for understanding current female engineers’intentions to stay or leave: engineering taskself-efficacy (the strength of a person’s belief in herability to complete tasks and reach goals related toher engineering job) and engineering task outcomeexpectations (the strength of a person’s belief that acertain action or behavior related to her engineeringwork will result in a positive outcome).To estimate current engineers’ levels of engineeringtask self-efficacy, the researchers lookedat the extent of study participants’ confidence on ascale of 1 (not at all confident) to 5 (very confident)in performing a variety of common engineeringtasks, such as “design a new product or project tomeet specified requirements” and “troubleshoota failure of a technical component or system.” Toestimate current engineers’ levels of outcome expectations,the researchers analyzed participant ratingsof their level of agreement, from 1 (strongly disagree)to 5 (strongly agree), with statements such as

AAUW 95“If I perform my job tasks well, then I will earn therespect of my co-workers,” “If I achieve in my job,I expect I’ll receive good raises,” and “When I amsuccessful at my work tasks, then my manager(s)will be impressed.”Analysis of the survey responses showed thatengineering task self-efficacy and positive outcomeexpectations were related both to higher jobsatisfaction and higher organizational commitment,which in turn were related to engineers’ greaterintention to stay in their jobs.What about men?Some have asked whether women and menrespond differently when it comes to workplaceenvironment—including self-efficacy and positiveoutcome expectations—and intentions to stay orleave engineering. Singh and Fouad responded bylaunching a similar study for men (at NSFgears.org). Preliminary results suggest that the samethings that affect women’s tendency to leave organizations—lackof opportunities for advancement,lack of opportunities for training and development,and excessive workloads—also affect men. SaysFouad, “We’re arguing that changing the environmentis good for everybody, men and women.”A better future for women (andmen) in engineering?Singh and Fouad believe that creating supportivework environments is possible. Singh told AAUW:A lot of the recommended changes thatcome out of our study have been put in placeby organizations often cited in lists of greatplaces to work. The recommendations thatfollow from our findings are not necessarilynovel or foreign. It’s just that some engineeringfirms may have been slow to acknowledgethe need for them. … What we reallyneed are systemic changes, an overhaul ofthe entire system.While not necessarily easy to change, reducingexcessive work demands and clarifying work goalsseem like possible changes for leaders of engineeringorganizations to make. But is an uncivil andundermining work environment changeable? Singhthinks so:There’s a lot of research on incivility andundermining behaviors in organizations.Organizations that have taken a very intentionaland purposeful approach to combatingthat have succeeded. These are all learnedbehaviors that, if they’re not dealt with andnegative consequences don’t follow, employeescome to understand that they can getaway with. The prevalence of incivility andundermining behaviors in an organizationreally stems from the organizational culture:whether the top leadership and whether theleadership at every level is tolerant or intolerantof these kinds of behaviors.What can organizationsdo?Self-efficacy and positive outcome expectationsmight at first glance appear to be individuallydeveloped and determined, but both characteristicsare influenced to a great degree by an individual’senvironment. “[Self-efficacy is] not somethingthat is fixed,” explains Singh. “It’s really malleable.It is context sensitive and can change in responseto certain elements in the work environment.” Sowhile some components of an individual’s engineeringtask self-efficacy and positive outcomeexpectations are specific to an individual’s psychologicalmakeup, both characteristics are decidedlyaffected by an individual’s workplace environment.For example, a person at one company might begiven the resources and authority to troubleshoot afailure and be rewarded if she solves the problem.That person is likely to have high self-efficacy andpositive outcome expectations. At another companythe same person might not be provided with theresources and authority to investigate the failureand will not be rewarded if she solves the problem.In the second case the person will likely have littleconfidence in her ability to succeed and will likelydevelop negative outcome expectations.Organizations can improve employees’ selfefficacyand outcome expectations through anumber of actions and policies. Organizations can

96SOLVING THE EQUATIONensure that employee roles and responsibilities areclearly defined, employees are provided with theresources they need to fulfill those responsibilities,and employees receive training and development.Organizations can acknowledge and rewardemployee contributions and ensure that employeesdo not have excessive workloads. Through theseefforts, organizations can improve self-efficacy andpositive outcome expectations among all employeesand in the process retain their female engineers.Chapter 9 notes1. Stemming the Tide (Fouad et al., 2012) was written for and funded by the National Science Foundation. Unlike the follow-uparticle (Singh et al., 2013), the 2012 report was not peer-reviewed. Despite that fact, the findings from the 2012 report areincluded here because of the importance of understanding factors that relate to the retention of women in engineering to theoverall issue of women’s underrepresentation in engineering and computing. Fouad, Singh, and their colleagues’ study (2012) isthe first to comprehensively investigate factors related to women’s decisions to leave or stay in engineering.2. The researchers compared responses from current engineers with those of engineers who had left the field in the previous fiveyears (rather than with those of women who had left at any point in their careers) to provide similar time frames for comparisonas well as to ensure that recollections were recent enough to be accurate.

Chapter 10.What Can We Do?

98SOLVING THE EQUATIONUnderrepresentation of women in engineeringand computing is a deeply rooted and complexsocial problem. But recent research and real-worldinitiatives have shown that there are ways to reducegender bias, increase the perceived and actualsocial relevance of engineering and computing, andultimately increase women’s sense of belonging inthese fields. Employers, educators, policy makers,and individuals can all take steps to improve women’srepresentation in engineering and computing.Reduce the influence ofgender biasReducing bias against women in engineering andcomputing fields is a society-wide endeavor. Thebest long-term strategy for accomplishing thisgoal is to change cultural stereotypes that lead togender biases—for example, that men are betterthan women at math, science, and the other skillsthat engineers and computing professionals need.Ironically, one way to change the operative stereotypesis for a critical mass of women to succeed inengineering and computing occupations (Eagly &Diekman, 2012), which brings us back to squareone.Change implicit biasesImplicit gender biases are more prevalent todaythan explicit gender biases are. While evidence issparse on how to change implicit biases in the longterm (Lai et al., 2013), positive role models appearto make a difference (Young, D. M., et al., 2013;Manke & Cohen, 2011; Asgari et al., 2010; Druryet al., 2011). A natural experiment in India, wherea law reserved village council leadership positionsfor women in randomly selected villages, illustratesthis finding. Researchers found that men in villagesthat were required to have female council leadersheld weaker implicit biases associating leadershipwith men than did men living in villages without agender quota (Beaman et al., 2009).Because implicit associations between mathand gender have been shown to be in place byage 7 or 8 (Cvencek, Meltzoff et al., 2011), ourbest chance to influence implicit biases may be toexpose girls and boys to positive female role modelsin engineering and computing early in life (Baronet al., 2014). Changing the formation of implicitbiases among children may be an effective approachfor reducing discrimination in the long term, butother tactics are needed to minimize the negativeeffects of gender bias in the workplace today. Whileeveryone has a role to play, employers occupy positionsof particular influence in this project.Change organizational practicesResearch points to a number of organizationalpractices that can help reduce the influence ofgender bias. For example, making job qualificationsclear and applying them evenly to all candidates,basing decisions on objective past performance,being aware of one’s own potential biases, andallowing sufficient time to make in-depth andindividualized evaluations can reduce the influenceof gender biases on hiring decisions (Uhlmann& Cohen, 2005; Isaac et al., 2009; Reuben et al.,2014a; Bendick & Nunes, 2012). Implementingthe following practices can also help reduce genderbias related to both hiring and retaining women inengineering and computing.Remove gender information fromcandidate evaluationsOne approach to reducing the influence of biasesin evaluations of others is to remove informationabout an individual’s age, race, and gender fromdecision-making contexts. A classic example ofthis approach relates to the hiring of professionalmusicians. In 1970 fewer than 10 percent of instrumentalistsin major U.S. symphony orchestras werewomen. Orchestra applicants typically competedfor positions by performing before an auditioncommittee. Because of concerns that selectionsmight be biased in favor of the students of certainrenowned teachers, several major U.S. orchestras inthe 1970s experimented with a new procedure thatinvolved placing a screen between the auditioningmusicians and the committee, so that judges couldhear but not see the applicants. In the 20 years followingthe adoption of blind auditions, the proportionof women hired by major symphony orchestrasdoubled—from approximately 20 percent toapproximately 40 percent (Goldin & Rouse, 2000).

AAUW 99Removing gender information from the applicationprocess allowed for a fairer hiring process. Ofcourse, hiding individuals’ characteristics is notalways feasible, but in certain situations, such aswhen an organization is evaluating résumés of jobapplicants, limiting gender information available toreviewers may help reduce gender discrimination.Hold managers accountableAccountability can encourage managers andrecruiters to rely less on gut instincts, which may bebased on unconscious biases, and exert more effortto gather relevant information and process theinformation more carefully. When individuals knowthat they will be held accountable for their actionsand decisions, they tend to act in ways that preparethem to justify their judgments. Stereotypes arean easy and efficient way to make decisions for themany professionals who are under time pressureor working on many tasks simultaneously, so whenevaluators are not held accountable for their judgments,they are likely to rely on stereotype-basedexpectations (Heilman, 2012).Emphasize that gender diversity is a goalResearch has found that a multicultural approach,a vision of diversity that celebrates social andcultural differences (Gutiérrez & Unzueta, 2010),is more effective in reducing bias than a colorblindapproach that ignores different group identities infavor of emphasizing an overarching organizationalidentity (Plaut et al., 2009; Stevens et al., 2008).A multicultural approach is also more effective atcreating environments in which minority groupsare likely to be engaged in their work and committedto organizational success (Purdie-Vaughns etal., 2008; Plaut et al., 2009).While the terms “multicultural” and “colorblind”pertain specifically to racial, ethnic, and culturaldiversity, the message is relevant for gender diversityas well. A colorblind approach with respect togender is akin to an organizational culture in whichissues of gender are rarely mentioned. Even if theorganization emphasizes the general importance ofdiversity, the importance of gender diversity, in particular,can be obscured because diversity is understoodto mean a number of different things (Bell& Hartmann, 2007; Unzueta et al., 2012). Whenorganizational leaders talk about the importanceof diversity, they should be clear that one desiredresult is recruiting and retaining more women.Introduce effective diversity initiativesDiversity does not automatically lead to betteroutcomes. A study of 700 private companies foundthat strategies in which responsibility and accountabilityfor diversity were clear, as when they wereassigned to a diversity committee or to a full-timediversity staff, were the most effective. Mentoringand networking interventions were moderatelyeffective as a means to reduce the social isolation ofpeople from nondominant groups. Diversity trainingor diversity evaluations that aimed to reducemanagerial bias were the least effective (Kalev et al.,2006).Nonetheless, some diversity training programshave been shown to reduce gender bias in theworkplace as well as in higher education settings,including in STEM-specific settings (Moss-Racusin, van der Toorn et al., 2014; Carnes, Devine,Isaac et al., 2012; Carnes, Devine, Manwell et al.,2014; Devine et al., 2012). One study (Catalyst,2012) examined the effect of diversity and inclusioneducation at a global engineering company andfound that a diversity training program had a transformativeeffect on those attending the program,shifting both their mindset and behavior, as well ashaving a positive effect on the workplace climate.Before attending the program, participants werenoncommittal about whether white men enjoyedprivileged status in U.S. society. Four months afterthe program, however, participants reported anincreased awareness of white male privilege. Theysaid that they were more likely to think criticallyabout social groups, take personal responsibilityfor being inclusive, consider other points of view,and listen empathetically. Co-workers noted someof these changes as well. Training was especiallysuccessful for participants who were not previouslyconcerned about being prejudiced, a group of peopleparticularly unlikely to recognize and accountfor their own biases without external intervention.Critical success factors in this training programincluded senior leader participation, a compelling

100SOLVING THE EQUATIONrationale that was clearly and persuasively communicatedbeforehand, ample opportunities forpractice, and an absence of blaming participants forinequality.This study is notable because significantshifts in beliefs are rarely found in attitudinalresearch. People’s beliefs about inequality areparticularly resistant to change (Hafer & Bègue,2005, in Catalyst, 2012). In addition, most of theparticipants were white men, a group of peoplewho have an important role in creating inclusivework environments because they hold a majorityof senior leadership positions in organizations,especially in engineering and technical organizations.Research finds that people tend to value andapprove of diversity-championing efforts whenthey are performed by white men but disapproveof the same efforts when performed by womenand other underrepresented groups (Hekman &Foo, 2014). The success of this effort points to thepossibility that diversity training can help organizationsachieve cultural change despite the finding byKalev and colleagues (2006) that diversity trainingis often ineffective. Clearly, diversity trainingprograms are not created equal, nor is the largercontext in the organization unimportant.Implement affirmative action policiesAffirmative action refers to “positive steps taken toincrease the representation of women and people ofother underrepresented groups in areas of employment,education, and culture from which they havebeen historically excluded” (Fullinwider, 2014).Affirmative action programs vary in their prescriptivenessfor employers. At one end of the spectrum,opportunity-focused affirmative action programsseek to expand recruitment of underrepresentedgroups or eliminate discrimination from the hiringand promotion process, while at the other end,quotas prescribe strong preferential treatment fromemployers (Harrison, D. A., et al., 2006).Examples of affirmative action policies includeplacing job opening announcements where morewomen and people of other underrepresentedgroups will be likely to see them and conductingoutreach programs designed to attract womenand people of other underrepresented groups.Federal agencies require that government contractorsdevelop affirmative action programs inwhich they establish goals to reduce or overcomeany instances in which their workforce has fewerwomen or people of other underrepresented groupsin a job than would reasonably be expected by theiravailability (U.S. Department of Labor, Office ofFederal Contract Compliance Programs, 2002).Affirmative action policies are tools thatrecruiters and managers can use to counteract theirgender biases so that they don’t, intentionally orunintentionally, keep women and people of otherunderrepresented groups out of engineering andcomputing jobs (Crosby, Iyer, & Sincharoen, 2006;Walton, Spencer et al., 2013). Affirmative actionpolicies are the only means of correcting discriminatoryinjustices in the United States that do notrely on the aggrieved parties coming forward ontheir own behalf (Crosby, Iyer, & Sincharoen,2006). This is especially important since manyemployees who are at a disadvantage on the basis ofdemographic characteristics, such as gender or race,may not consciously recognize problems related todiscrimination against their own group (Crosby,Iyer, Clayton et al., 2003).Some critics of affirmative action policiessuggest that such policies lower the quality of theselected individual or group; however, research onaffirmative action policies related to gender showsthat this is not true. Balafoutas and Sutter (2012)conducted a laboratory experiment where a varietyof affirmative action policies were applied to aseries of competitions. The presence of affirmativeaction policies, such as requiring a woman to beamong the winners and giving applicants “points”just because they were women, resulted in nosignificant differences in the overall performanceof the winner pool as compared to scenarios inwhich affirmative action policies were not used.The researchers found that the presence of affirmativeaction policies encouraged more highlyqualified women to enter the competition and didnot discourage men from competing, which meansthat although affirmative action policies resultedin more women being among the winners, womenwere rarely selected over more-qualified men.Affirmative action policies in this study encouraged

AAUW 101women to throw their hats into the ring, raising thecompetence of the applicant pool overall.Along the same lines, an examination of theeffects of political representation quotas in Swedenfound that gender quotas (reserving a certainpercentage of positions for women) increased thecompetence of the candidates overall and the groupof male candidates in particular, by reducing thenumber of less-qualified men running for politicaloffice (Besley et al., 2013). These studies suggestthat affirmative action policies help level the playingfield for applicants without reducing the talentthat employers can attract. Such policies are mostsuccessful in workplaces where executives supportthem and where affirmative action goals and policiesare clearly and persuasively communicated tostaff.Change individual behaviorsFor individual women in engineering and computing,navigating around systemic biases can be difficultif not impossible. Yet research points to certainsurvival skills for women in traditionally masculineenvironments that may be useful, although thesestrategies are not a substitute for addressing fundamentalissues of inequality.Research shows that when women in hiringsituations include clear evidence of competency butavoid appearing self-promoting in interviews, theyare viewed more positively. In addition, if womenuse only their first and middle initials instead oftheir first name (if it’s clearly a woman’s name)in their initial application, they may decrease thechance that gender bias will influence prospectiveemployers’ evaluations of their application (seeIsaac et al., 2009, for an overview of this literatureand original sources). Research also showsthat when women present their contributions asmotivated by the interests of the group ratherthan themselves, they tend to be perceived morepositively (Lucas & Baxter, 2012; Ridgeway, 1982;Shackelford et al., 1996).Specific to counteracting the negative effectsof stereotype threat, approaching interviews aschallenges in which the goal is to build skills ratherthan perform well has resulted in women feelingless threatened and performing better (Stout &Dasgupta, 2013). Self-affirmations and reappraisingnegative thoughts—for example, thinkingabout stressful situations in ways that allow oneto feel calm—have also been shown to protectwomen from the negative effects of stereotypethreat (Legault et al., 2012; Martens et al., 2006;Schmader, Forbes et al., 2009).Finally, just knowing about stereotype threathas been shown to lessen its effects. Simply understandingthat stereotypes are an external sourceof stress can protect women from the harmfuleffects of stereotype threat ( Johns et al., 2005;Dar-Nimrod & Heine, 2006).Make engineering andcomputing more sociallyrelevantStrategically incorporating activities that reflectcommunal values into engineering and computingcurricula and work—and, correspondingly, strategicallycommunicating the societal benefits ofengineering and computing to the public—is a wayto increase women’s representation in these fields.As Amanda Diekman told AAUW:People do not think STEM fields, in particularengineering and computing, provideopportunities for working with others andhelping others, so anything that can bedone to incorporate that kind of activity, tohighlight that and draw attention to it, willincrease people’s interest and engagement inthe domain and their ability to see themselvesas part of that field.Evidence suggests that highlighting the communalaspects of STEM careers increases girls’ interest inthese careers (Colvin et al., 2013; Tyler-Wood etal., 2012).Incorporate communal values intocollege curricula and cultureEvidence suggests that incorporating communalvalues into engineering and computing curricula isespecially beneficial for women. Vaz and colleagues(2013) found that Worcester Polytechnic Institute’s

102SOLVING THE EQUATIONlongstanding project-based learning curriculum,centered on getting students out of the classroomto solve real-life, open-ended problems, has beenparticularly effective for its female alumni. In asurvey of its engineering graduates during a 38-yearperiod, women were more likely than men to reportthat their project-based learning experiences hada positive impact across a wide variety of personaland professional development measures, includingunderstanding the connections between technologyand society, feeling connected to their community,and feeling as though they can make a difference.Engineering students who perceived thatethical and social issues were de-emphasized inengineering programs placed a low value on socialconsciousness and public welfare beliefs at the endof their college career. When students perceivedthat their engineering programs emphasized ethicaland social issues, however, they were more likely tobelieve that social consciousness and other publicwelfare measures were important (Cech, 2014).While more research is needed on this topic, thepreliminary lesson seems to be that when engineeringprograms incorporate a clear emphasis on ethicaland social issues, they produce engineers whoprioritize social responsibility.One additional way to attract more women toengineering and computing programs is to coupledegrees in these majors with degrees in other fieldsthat allow individuals to pursue multiple interests.For example, at Georgia Tech in 2014, 18 percentof bachelor’s degrees in computer science wereawarded to women, while women earned 45 percentof bachelor’s degrees in computational media,a joint degree between computing and the schoolof literature, media, and communications (Guzdial,2014).Incorporate communal valuesinto the workplaceCan employers do anything to attract individuals,including many women, who value working withand helping people to technical jobs that don’thave clear social contributions? For example, wouldproviding opportunities for mentoring students,junior-level engineers, or technical workers; buildingworkers’ awareness of the ultimate beneficiariesof their research or design efforts; or providing progressivework-family policies help engineering andcomputing workers fulfill their communal goals?As Diekman told AAUW:If your communal goals are not fulfilled atwork, then extra activities outside of workbecome especially important. Whether that’sfamily or caregiving or some kind of communityservice or volunteering, communalactivities that are outside of your engineeringcareer might actually have profound implicationsfor your engineering career, becauseif you can fulfill communal goals in otherplaces, then it may not be as important tohave them fulfilled at work.This research suggests that organizations mightbenefit from advancing progressive work-familypolicies or enabling employees to volunteer for asocial cause of their choice.In addition, individual engineers and computingprofessionals can prioritize the availability ofopportunities for communal goal achievementwhen they are looking for jobs. Diekman andher colleagues’ research suggests that consideringpotential for working with and helping others,along with technical aspects, when deciding on ajob can help communally oriented engineers andcomputing professionals find more job satisfaction.In tandem, the leaders of engineering and computingorganizations should be proactive in clarifyingthe value accorded to communal activities and beparticularly aware of the common subtle and overtdevaluing of those activities (Diekman, Weisgramet al., 2015).Cultivate a senseof belongingA sense of belonging has measurable effects onan individual’s physical and mental states. Evenminimal indications of social connectednesscan increase feelings of belonging (Cwir et al.,2011; Walton, Cohen et al., 2012). For women inengineering and computing, having a strong senseof belonging has been found to help alleviate thestress that arises from stereotype threat (Shnabel et

AAUW 103al., 2013; Richman et al., 2011; London, Rosenthalet al., 2011; London, Ahlqvist et al., 2014).One way to increase a sense of belongingamong women in computing is to introduce themto computing at an early age. Offering, and possiblyrequiring, computing courses in elementary, middle,and high school can result in more girls feeling thatthey belong in computing.Creating a genuinely welcoming environmentis a critical step for organizations seeking to attractand retain women and people of other underrepresentedgroups. Subtle cues can send a message towomen that they don’t belong in an environment.For example, exposure to gender-stereotypical commercialshas been shown to undermine women’saspirations and performance in math and science(Davies et al., 2002). As mentioned in chapter 2, astereotypically “geeky” masculine setting has beenshown to undermine women’s sense of belongingand interest in computing (Cheryan et al., 2009).Changing the representations people are exposedto, endorsing a philosophy that explicitly valuesAdvancing women in academicengineering and computingSurvey data and interviews with tenured professorsidentify a sense of community and the presence ofa support network as some of the most importantfactors in job satisfaction and retention of femaleSTEM faculty (Tyson & Borman, 2010; Young, 2012).The National Science Foundation’s Advance programhas funded the efforts of dozens of academicinstitutions to develop systemic approaches andinnovative ways to increase the participation andadvancement of women in academic science andengineering careers. Both academic research andstrategies developed by Advance programs, such asthe Stride program at the University of Michigan andthe WISELI program at the University of Wisconsin,consistently identify mentoring and effective, fairleadership as two areas that improve the workplaceexperience for female academics in scienceand engineering.the social identity of women (Derks et al., 2007),and increasing the representation and visibility ofwomen can create an environment in which womenfeel welcome, which can increase motivation, commitment,and persistence (Walton, Spencer et al.,2013).Research has identified a number of strategiesthat women in engineering and computing fieldsuse to increase their sense of belonging, not allof which are effective. In male-dominated workplaces,the culture sometimes encourages womento engage in “discursive positioning” (positioningoneself as an exception to the rule) by distancingthemselves from other women (Rhoton, 2011;Faulkner 2009a, 2009b; Servon & Visser, 2011).Individuals may also suppress their beliefs andopinions to better fit in. While this may workfor a while, self-silencing as a coping mechanismultimately results in individuals feeling alienatedand less motivated, which hinders performance andmay lead to disidentification with the work or field(London, Downey et al., 2012). Separating one’swork identity from one’s outside identity is anothermechanism women may use to fit into engineeringand computing work environments, but that canalso have negative results, such as increased depressionand lowered life satisfaction (von Hippel,Walsh et al., 2011).While these individual strategies are not effective,organizations can take steps to cultivate asense of belonging among women in engineeringand computing. For example, college engineeringor computing department educators can convey themessage that their program will require significanteffort from everyone, both women and men.In one study, when undergraduate women heardthat a program would require significant effortfrom everyone, they reported an increased senseof belonging in STEM fields (Smith, J. L., et al.,2013). Along the same lines, encouraging a “growthmindset” (a belief in the malleability of intelligenceand an awareness that difficulties and challengesare a normal part of earning an engineering orcomputer science degree and working in thesefields) has been shown to increase women’s sense ofbelonging (Walton & Cohen, 2007).

104SOLVING THE EQUATIONProviding an environment free from discriminationwhere women have social support is alsoimportant (Richman et al., 2011). Female engineersreport that having friendly social interactions withco-workers increases their feeling that they belong(Hatmaker, 2013). Finally, interventions to minimizedisparities and encourage positive relationsbetween groups (women and men, for example)have been shown to help promote engagement andprevent feelings of isolation and devaluation thatcan lead women to feel as if they don’t belong inengineering or computing fields (London, Ahlqvistet al., 2014).In sum, organizations, educators, and individualscan do many things to reduce bias, make engineeringand computing more socially relevant, andencourage a sense of belonging among women.RecommendationsTo increase women’s representation in engineeringand computing occupations, AAUW offers thefollowing recommendations, which are based onan extensive review of relevant research and on thefindings highlighted in this report.For employersEmployers are able to influence the representationof women in engineering and computing by changingthe workplace climate and hiring and promotionpractices. For more information, see chapters3 and 4 on bias in hiring and evaluations, chapter 6on the importance of communal values, and chapter9 on the workplace environment.Maintain good management practicesthat are fair and consistent and thatsupport a healthy work environment• Communicate clear responsibilities, goals, andpaths toward advancement.• Assign employees challenging projects that helpthem develop and strengthen new skills.• Provide training and development opportunitiesfor employees.• Acknowledge and reward employees’contributions.• Ensure that employees have manageable workloadsand are not expected to routinely workexcessive hours.• Provide and encourage the use of work-lifebalance support such as on-site daycare, flexiblework schedules, paid parental leave, andtelecommuting.• Provide opportunities for senior technical workersto mentor students or junior-level technicalworkers.• Put in place anti-harassment policies such asthat instituted by the Ada Initiative, adainitiative.org/what-we-do/conference-policies.• Work to establish welcoming environmentsthrough inclusive workplace policies.Manage and promote diversity andaffirmative action policies• Ensure that job advertisements, mission statements,and internal communications explicitlyconvey that your organization values diversityand gender inclusiveness.• Assign responsibility for diversity to a diversitycommittee or full-time diversity staff.• Involve men, especially white men, in genderdiversity efforts.• Conduct effective diversity training foremployees.• Monitor your progress in increasing women’srepresentation in technical roles.Reduce the negative effects ofgender bias• Make job qualifications clear and apply themevenly to all candidates.• Base hiring decisions on objective past performanceinformation when possible.• Purposely remove gender information fromevaluation scenarios when possible.• Allow sufficient time to make in-depth andindividualized evaluations of applicants.• Ensure that hiring managers and otheremployees are aware of their own potentialgender biases, such as by taking the genderscienceImplicit Association Test at implicit.harvard.edu.

AAUW 105• Survey employees to assess the level of genderbias within your organization.• Hold managers and recruiters accountable fortheir hiring and promotion decisions.Encourage a sense of belonging• Create a welcoming environment for allemployees.• Encourage a supportive, friendly, and respectfulenvironment.• Root out uncivil and undermining behaviors.• Increase the number of women at all levels ofmanagement.• Provide opportunities for women to develop asupport network of other technical women.• Formally recognize necessary nontechnical worksuch as working well with others and mentoring—workthat is not male-stereotyped—alongwith technical work.• Be proactive and vocal about management’scommitment to increasing the representation oftechnical women in your organization.Facilitate opportunities for employeesto work on projects or issues that aresocially relevant• Pursue projects with clear social impacts wheneverpossible.• Showcase how professionals’ everyday workaligns with the societally beneficial outcomesthat are the ultimate goals of engineering andtechnology.• Establish social service days where employeesvolunteer in their communities.For women working inengineering and computingWomen engineering and computing professionalsface challenges navigating stereotypes and genderbiases in environments in which they are often theminority. They are also well placed to attract otherwomen to these fields.• Seek a support network. Some possibilitiesinclude participating in a Society of WomenEngineers chapter, the Systers e-mail list(hosted by the Anita Borg Institute for Womenand Technology), or a women-in-engineering orcomputing group on campus or at work.• Prioritize working in jobs that allow you towork with others on socially relevant problemsif you place a high value on communal goals.• Seek opportunities to serve as a role model forgirls and young women considering engineeringand computing careers.• Share with students at all levels how you workwith and help people.For men working in engineeringand computingBecause they make up the majority of workers inengineering and computing, men play importantroles in creating the workplace climate and inrecruiting and influencing prospective professionals.Importantly, the recommendations for increasingthe representation of women in engineering andcomputing often benefit the men in these professionsas well.• Seek opportunities to serve as a role model forgirls and young women considering engineeringand computing.• Refuse to participate on all-male conferencepanels. Encourage conference organizers torecruit at least one female panelist.• Share with students at all levels how you workwith and help people.For educatorsEducators at all levels influence how studentsperceive the fields of engineering and computing,as well as how students view themselves. The followingrecommendations come from the literaturereviewed on bias (chapters 2, 3, and 4), stereotypethreat (chapters 2 and 5), and values and careerchoice (chapters 6, 7, and 8).• Spread the word that engineering skills andcompetencies are learned, not innate (in otherwords, cultivate a growth mindset). In engineeringand computing classrooms, reduce theassumption that technical competence is innateby reinforcing the idea that successful engineersor computing professionals are willing

106SOLVING THE EQUATIONto practice to develop their skills and persistthrough difficulties.• Frame adversity as a common experience foreveryone so that challenging coursework doesnot selectively signal to students that they donot belong in engineering or computing.• Teach students about the effects of stereotypethreat to lessen its effects.• Give a broad range of people exposure to computing.Move away from the idea that certainpeople (often with strong programming skills)are cut out for computing while others are not.• Highlight the broad applications of engineeringand computing.• Highlight the ways in which engineering andcomputing help people and provide opportunitiesfor working with others.• Provide opportunities for girls and youngwomen to interact with women and men withwhom they can identify in engineering andcomputing.• Create welcoming environments for girls inmath, science, engineering, and computing withgender-neutral decor; by endorsing a philosophythat explicitly values the social identity ofwomen; and by increasing the representationand visibility of girls and women.• Provide girls with opportunities to tinker andbuild confidence and interest in their design andprogramming abilities.For colleges and universitiesBecause most engineers and computing professionalsare trained in their professions in institutions ofhigher education, colleges and universities have aspecial role to play in increasing the representationof women in engineering and computing.engineering and computing professors• Emphasize the social impact of engineering andcomputing work.• Apply concepts that students are learningin class to community needs, incorporatingproject-based learning or service learningcomponents into engineering or computingcurricula.• Apply engineering and computing to realworldproblems.• Emphasize ethical and social issues when teachingengineering and computing.• Encourage a supportive environment in theclassroom and in the program.• Encourage and assist early contact betweenstudents and professionals.• Emphasize the wide variety of expertise necessaryto be successful as an engineer or computingprofessional.• Highlight as early as possible the different facetsthat make up engineering and computing.Engineering professors• Expand examples beyond those that involvestereotypically male applications such as cars orrockets. The NSF-funded Engage project has acollection of gender-neutral Everyday Examplesin Engineering that professors can use.• Introduce students to experiences in the fieldearly in undergraduate coursework to allowstudents to see the differences between textbookproblems and the creativity and critical thinkingnecessary for actual engineering problemsolving.Computing professors• Split classes by experience, providing studentswith less experience in computing with the timeand environment they need to build their skillsand interest.• Question the idea that certain people (oftenwith strong programming skills) are cut out forcomputing while others are not.• Send female students (a mix of students interestedin computing and those not consideringcomputing as a major) to the Grace HopperCelebration of Women in Computing or similarconferences. Taking even a few students canchange the mindset of those students, who canthen have a large effect on a program.

AAUW 107Social science professors• Conduct research on how to counteract theeffects of gender bias and the effects of diversityon outcomes.• Conduct more research in field settings, inengineering and computing workplaces, and inclassrooms.Administrators• Require researchers who receive federal funds toparticipate in bias training.• Require all undergraduate students to take atleast one computer science course, no matterwhat their major.• Provide opportunities for female students inengineering and computing to develop a supportnetwork of other technical women.• Offer and promote dual-degree programs forstudents interested in engineering or computingwho also have strong interests in other fields.• Engage in active public relations campaigns thatmake it clear to young women that engineersand technical professionals work cooperativelywith others on problems that have impactson the well-being of people, for example, byusing the National Academy of Engineering’sChanging the Conversation materials (2008).For policy makersPolicy makers can help improve the representationof women in engineering and computing througheducation programs and research funding, as wellas by ensuring that federally funded programscomply with civil rights laws designed to tackle sexdiscrimination. Congress enacted Title IX to makesure that federal resources are not used to supportdiscriminatory practices in education programsand to provide individual citizens effective protectionagainst such bias. In addition, state and localgovernments can adopt and promote education andworkplace policies that can narrow the achievementgap for girls and women in STEM.Federal government• The U.S. Department of Education should issueguidelines for Title IX coordinators that outlinetheir responsibilities for ensuring equity inSTEM education. The guidelines should coverconcerns from elementary and secondary educationthrough postdoctoral studies and workforcetraining. These guidelines should be broadlydisseminated and publicized.• The executive branch should lead efforts toincrease awareness, comprehensiveness, andtransparency of federal agency Title IX compliancereviews. Such reviews, which all federalagencies should conduct, not only those in theDepartment of Education, are critical to leveragingchange when recipients of federal fundsare found lacking in the placement, advancement,and retention of women in STEM disciplines.Compliance reviews and mechanismsfor enforcement of Title IX are available duringpre-award reviews, post-award compliancereviews, and investigations of complaints.• To comply with Title IX, federal agenciesshould ensure that educational institutionsreceiving grant funding or other financial assistanceprovide policies to maintain safe climatesto prevent sexual harassment (including genderbasedharassment and sexual assault) andnondiscriminatory policies for health insurancebenefits and other services.• Federal grant processes should allow for flexibilityrelative to academic engineers’ and computerscientists’ life events (such as birth or adoptionof a child), and paid family leave and paid sickdays should be encouraged.• Congress should direct and provide adequatefunding for federal, state, and local agencies toestablish outreach and retention programs at theelementary, secondary, and postsecondary levelsto engage women and girls in STEM activities,courses, and career development. For example,Congress should strengthen the gender-equityprovisions of the America Competes Actreauthorization, which authorizes science andtechnology research programs for five years andcontains provisions to support education andtraining aimed at addressing gender discriminationin the STEM fields.• Congress should ensure that federal laws, suchas the Carl D. Perkins Vocational and Technical

108SOLVING THE EQUATIONEducation Act, that fund and affect STEMeducation and workforce training also holdstates and programs accountable for movingwomen and girls into training that is nontraditionalby gender.• Congress should include in STEM educationlaws provisions for support services, such asdependent care, transportation assistance, careercounseling, tuition assistance, and other servicesthat allow individuals to successfully completetraining programs. In addition, federallyfunded career guidance and counseling must beprovided to all students and delivered in a fairmanner that ensures that students are receivingunbiased information about high-skill, highwagecareers in nontraditional fields.• Rising above the Gathering Storm, Revisited(National Academy of Sciences et al., 2010),commissioned by Congress, states that U.S.advantages in science and technology have begunto erode and discusses the need to improvemath and science education. Unfortunatelythe report largely ignores the issue of girlsand women in STEM fields. Congress shouldrequest a follow-up report on what effectincreasing the number of women in STEMfields would have on enabling the United Statesto remain a leader in the global marketplace.This should illustrate the important contributionswomen can make to STEM fields and putweight behind efforts to increase opportunitiesfor women and girls.• Additional funding should be provided to betterunderstand the underrepresentation of womenin engineering and computing and to developinterventions that increase the representation ofwomen in these fields.State and local governments• States should pass legislation to allow computingclasses taught in secondary education tocount toward graduation requirements.• States should establish high-quality, uniform,and rigorous K–12 education standards, suchas Common Core State Standards and NextGeneration Science Standards or equallyrigorous standards, to ensure that all studentsare taught to the same high expectations.• State and local education agencies must be heldaccountable for improving the successful accessto, and outcomes of women and girls in, careerand technical education programs, especiallyin programs that are nontraditional for womenand lead to high-skill, high-wage employment.• All education funding should include supportfor teacher training to include recognition ofimplicit gender bias, awareness of stereotypethreat, and ways to promote a growth mindsetin students.• All data regarding STEM study and workforceparticipation collected by state and federalgovernments should be disaggregated and crosstabulatedby gender and race.For parentsParents, like educators, influence how girls perceivethe fields of engineering and computing as well astheir own abilities and can encourage their daughtersto develop interest and confidence in thesefields. Parents also play an important role in exposingtheir children both to the fields of engineeringand computing generally and to women in thesefields at early ages, when their implicit biases areforming.• Cultivate a growth mindset in your children.Teach them that the brain is like a muscle thatgets stronger and works better the more it isexercised. Teach them that passion, dedication,and self-improvement, not simply innate talent,are the roads to genius and contribution.• Introduce your daughters to engineering andcomputing.• Encourage your daughters to pursue mathematicsand take calculus.• Introduce your children to women and menwith whom they can identify in engineering andcomputing fields.• Question the idea that certain people (oftenwith strong programming skills) are cut out forcomputing while others are not.• Provide girls with opportunities to tinker, takethings apart, and put them back together.

AAUW 109• Encourage your daughters to play and workwith boys.• Encourage your sons to play and work withgirls.For girlsGirls should learn about engineering and computingso that they can make informed decisions aboutwhether either of these fields is a good fit for theirabilities and interests.• Learn about the fields of engineering andcomputing. AAUW offers opportunities suchas Tech Trek and Tech Savvy programs thatprovide opportunities for learning about thesefields.• Get to know women in engineering andcomputing.• Tinker with things, take things apart, and putthem back together.• Cultivate a growth mindset. When you challengeyourself, work hard, and learn new things,your brain forms new connections, and overtime you become smarter.• Consider pursuing a dual-degree program incollege, coupling a major in engineering orcomputing with a major in another field suchas liberal arts or social science to allow in-depthpursuit of more than one interest.

Appendix

112SOLVING THE EQUATIONFigure A1. Engineering OccupationsFigure A2. Computing OccupationsAerospace engineersAgricultural engineersBiomedical engineersChemical engineersCivil engineersComputer hardware engineersElectrical and electronics engineersElectrical engineersElectronics engineers, except computerEnvironmental engineersIndustrial engineers, including health and safetyHealth and safety engineers, except mining safety engineersand inspectorsIndustrial engineersMarine engineers and naval architectsComputer and information research scientistsComputer and information analystsComputer systems analystsInformation security analystsSoftware developers and programmersComputer programmersSoftware developers, applicationsSoftware developers, systems softwareWeb developersDatabase and systems administrators and network architectsDatabase administratorsNetwork and computer systems administratorsComputer network architectsComputer support specialistsComputer user support specialistsComputer network support specialistsMiscellaneous computer occupationsMaterials engineersMechanical engineersNote: All occupations listed under “Computer Occupations” minor group 15-1100.Source: U.S. Department of Labor, Bureau of Labor Statistics (2009).Mining and geological engineers, including mining safetyengineersNuclear engineersPetroleum engineersMiscellaneous engineersNote: All occupations listed under “Engineers” minor group 17-2000.Source: U.S. Department of Labor, Bureau of Labor Statistics (2009).

AAUW 113FIGURE A3. PAY GAP IN SELECTED ENGINEERING AND COMPUTING OCCUPATIONS, 2013$120,000WomenMenxx% Women's earningsas a percentage ofmen's earnings$100,000Annual earnings$80,000$60,00092%91%87%85%87%87%90% 88%$40,00078%$20, 0000CivilianemployedpopulationComputerprogrammersComputersupportspecialistsSoftwaredevelopersComputersystemsanalystsIndustrialengineersElectricalengineersMechanicalengineersCivilengineersOCCUPATIONSNotes: Includes full-time, year-round civilian population. Not all computing and engineering occupations are included because of large margins of error associated with occupationsin which the numbers of workers (especially women) were relatively low. “Software developers” includes applications and systems software. “Industrial engineers” includes healthand safety. “Electrical engineers” includes electronics engineers.Source: L. M. Frehill analysis of U.S. Census Bureau (2014c).

114SOLVING THE EQUATIONFIGURE A4. ASSOCIATE DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1990–201370605058%50%60% 60%48%50%62% 62%52%56%61%58%46%42%Percentage4038%3029%24%21%2012%13%14%15%13%14%1001990 1995 2000 2005 2010 2013EngineeringAll science and engineeringComputingAllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; mathematics and computer science; physical sciences;psychology; social sciences; and engineering.Source: AAUW analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014a).

AAUW 115FIGURE A5. ENGINEERING AND COMPUTING BACHELOR’S DEGREES,BY RACE/ETHNICITY AND GENDER, 20137,0006,0008%6,184Engineering degreesComputing degreesx% percentage of totalbachelor's degreesawarded in field5,000Number of degrees4,0003,0009%4,0063%2,6509%3,8092,0003%1,4912%1,7231,0001%8552%91000.5% 0.4%0.2% 0.1% 20027667 80WomenMenWomenMenWomenMen(8%) (8%) (10%) (11%) (0.4%) (0.5%)BLACK HISPANIC AMERICAN INDIAN/ALASKA NATIVENotes: Parentheses show the percentage of 20–24-year-olds in the general population. Racial/ethnic groups include U.S. citizens and permanent residents only. Data based ondegree-granting institutions eligible to participate in Title IV federal financial aid programs.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2014b).

116SOLVING THE EQUATIONFigure A6a. Engineering Bachelor's Degrees Awarded to Womenat the 25 Largest U.S. Engineering Schools, 2012RankInstitutionWomen earningengineering degreesTotal engineeringdegrees conferredPercentage ofengineering degreesawarded to women1 Georgia Institute of Technology, Main Campus 346 1,663 21%2 Pennsylvania State University, Main Campus 248 1,462 17%3 Purdue University, Main Campus 293 1,391 21%4 Texas A&M University, College Station 260 1,346 19%5 University of Illinois, Urbana-Champaign 218 1,289 17%6 North Carolina State University, Raleigh 315 1,270 25%7 University of Michigan, Ann Arbor 276 1,198 23%8 Virginia Polytechnic Institute and State University 208 1,151 18%9 Ohio State University, Main Campus 172 1,128 15%10 University of Texas, Austin 232 1,065 22%11 University of Florida 223 1,062 21%12 California Polytechnic State University, San Luis Obispo 142 995 14%13 Iowa State University 132 898 15%14 University of California, Berkeley 197 883 22%15 Missouri University of Science and Technology 141 804 18%16 University of Minnesota, Twin Cities 155 801 19%17 Arizona State University 142 715 20%18 University of Washington, Seattle Campus 157 704 22%19 University of Maryland, College Park 112 703 16%20 University of California, San Diego 134 702 19%21 Auburn University 106 699 15%22 Rutgers University, New Brunswick 128 683 19%23 University of Central Florida 83 664 13%24 Rensselaer Polytechnic Institute 165 657 25%25 University of Wisconsin, Madison 125 656 19%Note: Ranking includes only institutions that conferred at least 20 engineering bachelor’s degrees in 2012.Source: L. M. Freehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013).

AAUW 117Figure A6b. Engineering Bachelor’s Degrees Awarded to Women by U.S.Engineering Schools with the Highest Representation of Women, 2012RankInstitutionWomen earningengineering degreesTotal engineeringdegrees conferredPercentage ofengineering degreesawarded to women1 Smith College 22 22 100%2 Prairie View A&M University 69 105 66%3 Harvard University 28 60 47%4 Massachusetts Institute of Technology 201 453 44%5 California Institute of Technology 44 101 44%6 Howard University 24 56 43%7 George Washington University 40 94 43%8 Tuskegee University 18 43 42%9 Franklin W. Olin College of Engineering 28 69 41%10 Rice University 83 215 39%11 Harvey Mudd College 25 65 38%12 Stanford University 125 335 37%13 Humboldt State University 13 35 37%14 Princeton University 64 177 36%15 Yale University 24 67 36%16 Tulane University of Louisiana 16 45 36%17 Northwestern University 108 305 35%18 University of Pennsylvania 100 286 35%19 University of Alaska, Anchorage 20 58 34%20 Hope College 11 32 34%21 Cornell University 196 577 34%22 Tufts University 65 192 34%23 North Carolina A&T State University 61 183 33%24 Eastern Michigan University 15 45 33%25 University of Puerto Rico, Mayagüez 193 580 33%Note: Ranking includes only institutions that conferred at least 20 engineering bachelor’s degrees in 2012.Source: L. M. Freehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013).

118SOLVING THE EQUATIONFigure A6c. Computing Bachelor’s Degrees Awarded to Womenat the 25 Largest U.S. Computing Schools, 2012RankInstitutionWomen earningcomputing degreesTotal computingdegrees conferredPercentage ofcomputing degreesawarded to women1 University of Phoenix (38 campuses)* 838 3,288 26%2 DeVry University (25 campuses)* 297 1,667 18%3 ITT Technical Institute (86 campuses)* 209 1,512 14%4 Art Institute (33 campuses)* 368 1,009 37%5 University of Maryland, University College 220 806 27%6 Strayer University (17 campuses)* 193 683 28%7 Pennsylvania State University, Main Campus 140 582 24%8 Kaplan University, Davenport Campus* 152 553 27%9 Western Governors University 49 518 9%10 ECPI University* 79 497 16%11 Westwood College (17 campuses)* 93 419 22%12 Full Sail University* 68 375 18%13 Rochester Institute of Technology 27 363 7%14 George Mason University 60 348 17%15 American Intercontinental University Online* 83 320 26%16 Purdue University, Main Campus 46 297 15%17 University of Maryland, Baltimore County 43 278 15%18 Arizona State University 40 261 15%19 American Public University System* 38 258 15%20 University of Maryland, College Park 57 255 22%21 Indiana University, Bloomington 47 234 20%22 Bellevue University 36 207 17%23 Capella University* 55 207 27%24 University of Central Florida 15 204 7%25 Rutgers University, New Brunswick 32 201 16%Note: Asterisks indicate private, for-profit institutions. Ranking includes only institutions that conferred at least 20 computing bachelor’s degrees in 2012.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013)

AAUW 119Figure A6d. Computing Bachelor’s Degrees Awarded to Women at U.S.Computing Schools with the Highest Representation of Women, 2012Rank1InstitutionInternational Academy of Design and Technology,Online Campus*Women earningcomputing degreesTotal computingdegrees conferredPercentage ofcomputing degreesawarded to women14 21 67%2 Johnson C. Smith University 15 23 65%3 Westwood College, Chicago Loop* 12 22 55%4 Academy of Art University* 31 58 53%5 Art Institute of California/Argosy University, San Diego* 40 76 53%6Art Institute of California/Argosy University,Sacramento*11 21 52%7 Southern University and A&M College 12 23 52%8 Art Institute of Las Vegas* 18 35 51%9 North Carolina Central University 13 28 46%10 North Carolina Wesleyan College 11 24 46%11 Northwest Missouri State University 11 24 46%12 Art Institute of Phoenix* 16 36 44%13 Ohio University, Main Campus 86 195 44%14 Guilford College 11 25 44%15 Indiana Wesleyan University 11 25 44%16 Quinnipiac University 17 39 44%17 Kentucky State University 9 21 43%18 Art Institute of Pittsburgh, Online Division* 20 49 41%19 Harvey Mudd College 13 32 41%202122Art Institute/Miami International University of Artand Design*Art Institute of California/Argosy University,Orange County*Art Institute of California/Argosy University,Inland Empire*17 42 40%19 47 40%16 40 40%23 Brandeis University 8 20 40%24 Art Institute of Seattle* 14 36 39%25 Grand View University 10 26 38%Note: Asterisks indicate private, for-profit institutions. Ranking includes only institutions that conferred at least 20 computing bachelor’s degrees in 2012.Source: L. M. Frehill analysis of National Science Foundation, National Center for Science and Engineering Statistics (2013).

120SOLVING THE EQUATIONFIGURE A7. COMPUTING OCCUPATIONS, CURRENT (2012) AND PROJECTED (2022) EMPLOYMENT80070023%20122022xx% change between2012 and 202225%20%60050020%Employment (1,000s)40012%8%30020020%15%7%15%10037%15%0Information securityanalystsComputer systemsanalystsSoftware developers,applicationsSoftware developers,systems softwareComputer user supportspecialistsWeb developersDatabase administratorsComputer and informationresearch scientistsComputer networkarchitectsNetwork and computersystems administratorsComputer programmersComputer networksupport specialistsOCCUPATIONSNotes: Percentages are projected growth in number of workers in each occupation defined as a computer occupation by the U.S. Department of Labor, 2012–2022. National growthin computer occupations overall is expected to be 18 percent and in all occupations is expected to be 11 percent.Source: L. M. Frehill analysis of data from U.S. Department of Labor, Bureau of Labor Statistics (2014d).

AAUW 121FIGURE A8. ENGINEERING OCCUPATIONS, CURRENT (2012) AND PROJECTED (2022) EMPLOYMENT35020%4%20122022xx% changebetween2012 and20223005%5%250Employment (1,000s)2001504%1007% 7%15%5027%26%9%5%1%0BiomedicalPetroleumCivilEnvironmentalMining and geological12% 10%Marine engineers andnaval architectsNuclearComputer hardwareAerospaceIndustrial (including healthand safety)OCCUPATIONSAgricultural5%ChemicalMechanicalElectrical and electronicsEngineers, all otherMaterialsNotes: Percentages are projected percentage growth in number of workers in each occupation included under the heading “Engineers” by the U.S. Department of Labor, 2012–2022.National growth in engineering occupations overall is expected to be 9 percent and in all occupations is expected to be 11 percent.Source: L. M. Frehill analysis of data from U.S. Department of Labor, Bureau of Labor Statistics (2014d).

122SOLVING THE EQUATIONFIGURE A9. ENGINEERING FACULTY, BY RANK, GENDER, AND RACE/ETHNICITY, 20131003%7%13%5%11%2%7%5%1%2%805%22%5%24%26%6063%Percentage45%53%40200Assistant professorAssociate professorFull professorURM women Asian women White women URM men Asian menWhite menNotes: Underrepresented minority (URM) includes black, American Indian/Alaska Native, and Hispanic/Latino. Data shown are for the 328 participating engineering schools thatreported faculty data broken down by gender, race/ethnicity, and rank.Source: L. M. Frehill analysis of data from American Society for Engineering Education (2014).

AAUW 123FIGURE A10. COMPUTING FACULTY, BY RANK, GENDER, AND RACE/ETHNICITY, 20131009%3%7%2%11%0.4%2%12%16%24%2%804%26%5%21%6061%Percentage46%50%40200Assistant professorAssociate professorFull professorURM women Asian women White women URM men Asian menWhite menNotes: Underrepresented minority (URM) includes black, American Indian/Alaska Native, and Hispanic/Latino. Data shown are for the 143 participating computer sciencedepartments that reported faculty data broken down by gender, race/ethnicity, and rank. Responses from individuals who indicated that they were multiracial or nonresident aliensand those whose ethnicity is unknown or not reported were not included.Source: L. M. Frehill analysis of data from Computing Research Association (2014).

124SOLVING THE EQUATIONFigure A11. Academic Disciplines Included in EngineeringClassificationof InstructionalDisciplinePrograms (CIP) CodeEngineering, general 14.0101Aerospace, aeronautical andastronautical/space engineering14.0201Agricultural engineering 14.0301Architectural engineering 14.0401Bioengineering and biomedicalengineering14.0501Chemical engineering 14.07Civil engineering 14.08Computer engineering, general 14.0901Computer software engineering 14.0903Electrical, electronics andcommunications engineering14.10Environmental/environmental healthengineering14.1401Mechanical engineering 14.1901Nuclear engineering 14.2301Petroleum engineering 14.2501Systems engineering 14.2701Materials science and engineeringMaterials engineering 14.1801Materials science 40.1001Industrial engineering and managementIndustrial engineering 14.3501Engineering/industrial management 15.1501DisciplineAll otherClassificationof InstructionalPrograms (CIP) CodePre-engineering 14.0102Ceramic sciences and engineering 14.0601Computer engineering, other 14.0999Engineering mechanics 14.1101Engineering physics/applied physics 14.1201Engineering science 14.1301Metallurgical engineering 14.2001Mining and mineral engineering 14.2101Naval architecture and marineengineering14.2201Ocean engineering 14.2401Textile sciences and engineering 14.2801Polymer/plastics engineering 14.3201Construction engineering 14.3301Forest engineering 14.3401Manufacturing engineering 14.3601Surveying engineering 14.3801Geological/geophysical engineering 14.3901Paper science and engineering 14.4001Electromechanical engineering 14.4101Biochemical engineering 14.4301Engineering chemistry 14.4401Biological/biosystems engineering 14.4501Engineering, other 14.9999Geographic information science 45.0702Source: U.S. Department of Education, National Center for Education Statistics (2010).

AAUW 125Figure A12. Academic Disciplines Included in ComputingDisciplineClassification of InstructionalPrograms (CIP) CodeComputer and information sciences, general 11.01Computer programming 11.02Data processing 11.03Information science/studies 11.04Computer systems analysis 11.05Computer science 11.07Computer software and media applications 11.08Computer systems networking and telecommunications 11.09Computer/information technology administration and management 11.10Computer and information sciences and support services, other 11.99Notes: CIP code 11.06 (data entry/microcomputer applications) is the only discipline that the U.S. Department of Education includes under CIP code11 that is not included here. CIP codes 11.03 and 11.99 are combined for the category “computer/information science support services, including dataprocessing” in figure 8.Source: U.S. Department of Education, National Center for Education Statistics (2010).

126SOLVING THE EQUATIONFIGURE A13. MASTER’S DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1970–2013706058%59%60%60%53%55%5050%50%49%45%43%44%46%4040%38%Percentage32%34%33%3028%29%28%26%29%28%27%22%21%21%23%22%24%2018%15%14%16%11%109%7%1%3%01970 1975 1980 19851990 1995 2000 2005 2010 2013Engineering Computing All science and engineering AllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; mathematics and computer science; physical sciences;psychology; social sciences; and engineering.Source: L. M. Frehill analysis of data from National Science Foundation, Division of Science Resources Statistics (2013), and National Science Foundation, National Center forScience and Engineering Statistics (2014a).

AAUW 127FIGURE A14. DOCTORAL DEGREES EARNED BY WOMEN, SELECTED FIELDS, 1970–201370605044%45%47%50%45%Percentage403030%34%26%36%28%39%31%36%38%41%2010022%22%16%14%11%10%9%6%4%2%0.4%0%0%1970 1975 1980 198523% 23%20%19%21%17%16%19%18%16%12%9%1990 1995 2000 2005 2010 2013Engineering Computing All science and engineering AllNote: “All science and engineering” includes biological and agricultural sciences; earth, atmospheric, and ocean sciences; mathematics and computer science; physical sciences;psychology; social sciences; and engineering.Source: L. M. Frehill analysis of data from National Science Foundation, Division of Science Resources Statistics (2013) and National Science Foundation, National Center forScience and Engineering Statistics, (2014a).

128SOLVING THE EQUATIONReferencesAAUW. (2010). Why so few? Women in science, technology, engineering,and mathematics. Washington, DC: Author.———. (2012). Graduating to a pay gap: The earnings of womenand men one year after college graduation. Washington, DC:Author.———. (2014). The simple truth about the gender pay gap.Washington, DC: Author.Abbate, J. (2012). Recoding gender: Women’s changing participationin computing. Cambridge, MA: MIT Press.Alvarado, C., & Dodds, Z. (2010, March 10–13). Women inCS: An evaluation of three promising practices. SIGCSE 10:Proceedings of the 41st ACM technical symposium on computerscience education, 57–61. New York, NY: Association forComputing Machinery. doi:10.1145/1734263.1734281.Alvarado, C., Dodds, Z., & Libeskind-Hadas, R. (2012).Increasing women’s participation in computing atHarvey Mudd College. ACM Inroads, 3(4), 55–64.doi:10.1145/2381083.2381100.Alvarado, C., & Judson, E. (2014). Using targeted conferences torecruit women into computer science. Communications ofthe ACM, 57(3), 70–7. doi:10.1145/2500883.American Society for Engineering Education. (2005). The yearin numbers, by M. Gibbons. In Profiles of Engineering andEngineering Technology Colleges. Washington, DC: Author.———. (2014). Engineering by the numbers, by B. L. Yoder. InProfiles of Engineering and Engineering Technology Colleges.Washington, DC: Author.Anand, R., & Winters, M.-F. (2008). A retrospective view ofcorporate diversity training from 1964 to the present.Academy of Management Learning and Education, 7(3),356–72. doi:10.5465/AMLE.2008.34251673.Anita Borg Institute for Women & Technology. (2014a). TheAnita Borg Institute and Harvey Mudd College launchinitiative to increase the diversity in computer scienceundergraduate majors at 2014 Clinton Global Initiativeannual meeting [Press release].———. (2014b). Grace Hopper Celebration of Women inComputing: 2013 evaluation and impact report, by C. B.Simons, M. Miller, and G. Lichtenstein.Apple Inc. (2014). Inclusion inspires innovation. www.apple.com/diversity.Armstrong, P. I., Fouad, N. A., Rounds, J., & Hubert, L. (2010).Quantifying and interpreting group differences in interestprofiles. Journal of Career Assessment, 18(2), 115–32.doi:10.1177/1069072709354367.Aronson, J., Fried, C. B., & Good, C. (2002). Reducingthe effects of stereotype threat on African Americancollege students by shaping theories of intelligence.Journal of Experimental Social Psychology, 38(2), 113–125.doi:10.1006/jesp.2001.1491.Asgari, S., Dasgupta, N., & Cote, N. G. (2010). When doescontact with successful ingroup members changeself-stereotypes? A longitudinal study comparingthe effect of quantity vs. quality of contact with successfulindividuals. Social Psychology, 41(3), 203–11.doi:10.1027/1864-9335/a000028.Ayre, M., Mills, J., & Gill, J. (2013). “Yes, I do belong”: Thewomen who stay in engineering. Engineering Studies, 5(3),216–32. doi:10.1080/19378629.2013.855781.Baillie, C., & Levine, M. (2013). Engineering ethics from a justiceperspective: A critical repositioning of what it meansto be an engineer. International Journal of Engineering,Social Justice, and Peace, 2(1), 10–20.Balafoutas, L., & Sutter, M. (2012). Affirmative action policiespromote women and do not harm efficiency in thelaboratory. Science, 335(6068), 579–82. doi:10.1126/science.1211180.Banaji, M. R., & Greenwald, A. G. (2013). Blindspot: Hiddenbiases of good people. New York, NY: Delacorte Press.Bandura, A. (1982). Self-efficacy mechanism in humanagency. American Psychologist, 37(2), 122–47.doi:10.1037/0003-066X.37.2.122.———. (1997). Self-efficacy: The exercise of control. New York,NY: Freeman.Baron, A. S., Schmader, T., Cvencek, D., & Meltzoff, A. N.(2014). The gendered self-concept: How implicit genderstereotypes and attitudes shape self-definition. In P. J.Leman & H. R. Tenenbaum (Eds.), Gender and development(pp. 109–32). East Sussex, England: PsychologyPress.Beaman, L., Chattopadhyay, R., Duflo, E., Pande, R., &Topalova, P. (2009). Powerful women: Does exposurereduce bias? Quarterly Journal of Economics, 124(4),1497–540. doi:10.1162/qjec.2009.124.4.1497.Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C.(2010). Female teachers’ math anxiety affects girls’ mathachievement. Proceedings of the National Academy of Sciences,107(5), 1860–63. doi:10.1073/pnas.0910967107.Bell, J. M., & Hartmann, D. (2007). Diversity in everydaydiscourse: The cultural ambiguities and consequences of“happy talk.” American Sociological Review, 72(6), 895–914.doi:10.1177/000312240707200603.Benbow, C. P. (2012). Identifying and nurturing future innovatorsin science, technology, engineering, and mathematics:A review of findings from the study of mathematicallyprecocious youth. Peabody Journal of Education, 87(1),16–25.Bendick, M., Jr., & Nunes, A. P. (2012). Developing the researchbasis for controlling bias in hiring. Journal of Social Issues,68(2), 238–62. doi:10.1111/j.1540-4560.2012.01747.Berdahl, J. L. (2007). Harassment based on sex: Protectingsocial status in the context of gender hierarchy. Academy ofManagement Review, 32(2), 641–58.

AAUW 129Berdahl, J. L., & Moore, C. (2006). Workplace harassment:Double jeopardy for minority women. Journal of AppliedPsychology, 91(2), 426–36.Besley, T., Folke, O., Persson, T., & Rickne, J. (2013). Genderquotas and the crisis of the mediocre man: Theory and evidencefrom Sweden (IFN working paper no. 985). Stockholm,Sweden: Research Institute of Industrial Economics.Bilimoria, D., Joy, S., & Liang, X. (2008). Breaking barriers andcreating inclusiveness: Lessons of organizational transformationto advance women faculty in academic science andengineering. Human Resource Management, 47(3), 423–41.doi:10.1002/hrm.20225.Bix, A. S. (2002). Equipped for life: Gendered technical trainingand consumerism in home economics, 1920–1980.Technology and Culture, 43(4), 728–54. doi:10.1353/tech.2002.0152.———. (2014). Girls coming to tech! A history of Americanengineering education for women (Engineering studies).Cambridge, MA: MIT Press.Blascovich, J., Spencer, S. J., Quinn, D., & Steele, C. (2001).African Americans and high blood pressure: The role ofstereotype threat. Psychological Science, 12(3), 225–29.Bobbitt-Zeher, D. (2011). Gender discrimination at work:Connecting gender stereotypes, institutional policies, andgender composition of workplace. Gender and Society,25(6), 764–86. doi:10.1177/0891243211424741.Bosson, J. K., Haymovitz, E. L., & Pinel, E. C. (2004). Whensaying and doing diverge: The effects of stereotype threaton self-reported versus non-verbal anxiety. Journal ofExperimental Social Psychology, 40(2), 247–55. doi:10.1016/S0022-1031(03)00099-4.Bowling, N. A., & Beehr, T. A. (2006). Workplace harassmentfrom the victim’s perspective: A theoretical modeland meta-analysis. Journal of Applied Psychology, 91(5),998–1012.Brescoll, V. L., Glass, J., & Sedlovskaya, A. (2013). Ask and yeshall receive? The dynamics of employer-provided flexiblework options and the need for public policy. Journal ofSocial Issues, 69(2), 367–88. doi:10.1111/josi.12019.Brescoll, V. L., Uhlmann, E. L., Moss-Racusin, C., & Sarnell,L. (2012). Masculinity, status, and subordination: Whyworking for a gender stereotype violator causes men tolose status. Journal of Experimental Social Psychology, 48(1),354–57. doi:10.1016/j.jesp.2011.06.005.Buse, K., Bilimoria, D., & Perelli, S. (2013). Why they stay:Women persisting in US engineering careers. CareerDevelopment International, 18(2), 139–54. doi:10.1108/CDI-11-2012-0108.Camacho, M. M., & Lord, S. M. (2011, October 12–15).“Microaggressions” in engineering education: Climate forAsian, Latina, and white women. Proceedings of the 41stASEE/IEEE Frontiers in Education Conference, RapidCity, SD.Camp, T. (1997). The incredible shrinking pipeline.Communications of the ACM, 40(10), 103–10.doi:10.1145/262793.262813.Campbell, L. G., Mehtani, S., Dozier, M. E., & Rinehart, J.(2013). Gender-heterogenous working groups producehigher quality science. PLOS ONE, 8(10), e79147.doi:10.1371/journal.pone.0079147.CareerBliss. (2014). CareerBliss 50 happiest companies inAmerica for 2014. www.careerbliss.com/facts-and-figures/careerbliss-50-happiest-companies-in-america-for-2014.Carnes, M., Devine, P. G., Isaac, C., Manwell, L. B., Ford, C. E.,Byars-Winston, A., . . . & Sheridan, J. (2012). Promotinginstitutional change through bias literacy. Journal ofDiversity in Higher Education, 5(2), 63–77. doi:10.1037/a0028128.Carnes, M., Devine, P. G., Manwell, L. B., Byars-Winston, A.,Fine, E., Ford, C. E., . . . & Sheridan, J. (2014, November4). The effect of an intervention to break the gender biashabit for faculty at one institution: A cluster randomized,controlled trial. Academic Medicine. doi:10.1097/ACM.0000000000000552.Carnevale, A. P., Strohl, J., & Melton, M. (2011). What’s itworth? The economic value of college majors. Washington,DC: Georgetown University Center on Education and theWorkforce.Castilla, E. J., & Benard, S. (2010). The paradox of meritocracyin organizations. Administrative Science Quarterly, 55,543–76. doi:10.2189/asqu.2010.55.4.543.Catalyst. (2004). The bottom line: Connecting corporate performanceand gender diversity. New York, NY: Author.———. (2008). Women in technology: Maximizing talent, minimizingbarriers. New York, NY: Author.———. (2011). The bottom line: Corporate performance andwomen’s representation on boards (2004–08), By N. Carter &H. Wegner. New York, NY: Author.———. (2012). Calling all white men: Can training help createinclusive workplaces? By J. Prime, H. Foust-Cummings, E.R. Salib, & C. A. Moss-Racusin. New York, NY: Author.———. (2013).Why diversity matters. New York, NY: Author.———. (2014). High potentials in tech-intensive industries: Thegender divide in business roles, by A. Beninger. New York,NY: Author.Cech, E. A. (2014). Culture of disengagement in engineeringeducation? Science, Technology, and Human Values, 39(1),42–72. doi:10.1177/0162243913504305.Cech, E. A., Rubineau, B., Silbey, S., & Seron, C. (2011).Professional role confidence and gendered persistence inengineering. American Sociological Review, 76(5), 641–66.doi:10.1177/0003122411420815.Ceci, S. J., Ginther, D. K., Kahn, S., & Williams, W. M. (2014).Women in academic science: A changing landscape.Psychological Science in the Public Interest, 15(3), 75–141.doi:10.1177/1529100614541236.Cha, Y. (2013). Overwork and the persistence of gender segregationin occupations. Gender and Society, 27(2), 158–84.doi:10.1177/0891243212470510.Chamberlain, L. J., Crowley, M., Tope, D., & Hodson,R. (2008). Sexual harassment in organizational

130SOLVING THE EQUATIONcontext. Work and Occupations, 35(3), 262–95.doi:10.1177/0730888408322008.Charles, M. (2011). What gender is science? Contexts, 10(2),22–28. doi:10.1177/1536504211408795.Charles, M., & Bradley, K. (2009). Indulging our genderedselves? Sex segregation by field of study in 44 countries.American Journal of Sociology, 114(4), 924–76.Chen, J. J. (2008). Ordinary vs. extraordinary: Differential reactionsto men’s and women’s prosocial behavior in the workplace(Doctoral dissertation). New York University.Chen, X. (2013). STEM attrition: College students’ paths intoand out of STEM fields (NCES 2014-001). Washington,DC: U.S. Department of Education, National Center forEducation Statistics.Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009).Ambient belonging: How stereotypical cues impact genderparticipation in computer science. Journal of Personality andSocial Psychology, 97(6), 1045–60. doi:10.1037/a0016239.Claris, L., & Riley, D. (2012). Situation critical: Critical theoryand critical thinking in engineering education. EngineeringStudies, 4(2), 101–20. doi:10.1080/19378629.2011.649920.Code.org. (2014). Promote computer science. code.org/promote.College Board. (2013a). AP data—Archived data 2013. research.collegeboard.org/programs/ap/data/archived/2013.———. (2013b). 2013 college-bound seniors: Total groupprofile report. media.collegeboard.com/digitalServices/pdf/research/2013/TotalGroup-2013.pdf.Colvin, W., Lyden, S., & León de la Barra, B. (2013). Attractinggirls to civil engineering through hands-on activities thatreveal the communal goals and values of the profession.Leadership and Management in Engineering, 13(1), 35–41.doi:10.1061/(ASCE)LM.1943-5630.0000208.Computing Research Association. (2005, May). 2003–04Taulbee survey, by S. Zweben. Computing Research News,17(3). archive.cra.org/statistics/survey/04/04.pdf.———. (2014). 2012–13 Taulbee survey. Computing ResearchNews, 26(5). cra.org/resources/taulbee.Cortina, L. M., Kabat-Farr, D., Leskinen, E. A., Huerta,M., & Magley, V. (2013). Selective incivility as moderndiscrimination in organizations: Evidence andimpact. Journal of Management, 39(6), 1579–605.doi:10.1177/0149206311418835.Cortina, L. M., Magley, V. J., Williams, J. H., & Langhout, R. D.(2001). Incivility in the workplace: Incidence and impact.Journal of Occupational Health Psychology, 6(1), 64–80.doi:10.1037//1076-8998.6.1.64.Croizet, J. C., Després, G., Gauzins, M. E., Huguet, P., Leyens,J. P., & Méot, A. (2004). Stereotype threat underminesintellectual performance by triggering a disruptive mentalload. Personality and Social Psychology Bulletin, 30(6),721–31. doi:10.1177/0146167204263961.Crosby, F. J., Iyer, A., & Sincharoen, S. (2006). Understandingaffirmative action. Annual Review of Psychology, 57,585–611. doi:10.1146/annurev.psych.57.102904.190029.Crosby, F. J., Iyer, A., Clayton, S., & Downing, R. A.(2003). Affirmative action: Psychological data and thepolicy debates. American Psychologist, 58(2), 93–115.doi:10.1037/0003-066X.58.2.93.Cuddy, A. J. C., Fiske, S. T., & Glick, P. (2007). The BIASmap: Behaviors from intergroup affect and stereotypes.Journal of Personality and Social Psychology, 92(4), 631–48.doi:10.1037/0022-3514.92.4.631.Cvencek, D., Greenwald, A. G., & Meltzoff, A. N. (2011).Measuring implicit attitudes of 4-year-olds: The PreschoolImplicit Association Test. Journal of ExperimentalChild Psychology, 109(2), 187–200. doi:10.1016/j.jecp.2010.11.002.Cvencek, D., & Meltzoff, A. N. (2012). Math-gender stereotypesin elementary school children. In J. A. Banks (Ed.),Encyclopedia of diversity in education (Vol. 3, pp. 1455–60).Thousand Oaks, CA: SAGE.Cvencek, D., Meltzoff, A. N., & Greenwald, A. G.(2011). Math-gender stereotypes in elementaryschool children. Child Development, 82(3), 766–79.doi:10.1111/j.1467-8624.2010.01529.x.Cwir, D., Carr, P. B., Walton, G. M., & Spencer, S. J. (2011).Your heart makes my heart move: Cues of social connectednesscause shared emotions and physiological statesamong strangers. Journal of Experimental Social Psychology,47(3), 661–64. doi:10.1016/j.jesp.2011.01.009.Dar-Nimrod, I., & Heine, S. J. (2006). Exposure to scientifictheories affects women’s math performance. Science,314(5798), 435. doi:10.1126/science.1131100.Dasgupta, N. (2013). Implicit attitudes and beliefs adapt to situations:A decade of research on the malleability of implicitprejudice, stereotypes, and the self-concept. Advances inExperimental Social Psychology, 47, 233–79. doi:10.1016/B978-0-12-407236-7.00005-X.Davies, P. G., Spencer, S. J., Quinn, D. M., & Gerhardstein, R.(2002). Consuming images: How television commercialsthat elicit stereotype threat can restrain women academicallyand professionally. Personality and Social PsychologyBulletin, 28(12), 1615–28. doi:10.1177/014616702237644.De Houwer, J., Teige-Mocigemba, S., Spruyt, A., & Moors,A. (2009). Implicit measures: A normative analysis andreview. Psychological Bulletin, 135(3), 347–68. doi:10.1037/a0014211.Denissen, A. M. (2010). Crossing the line: How women in thebuilding trades interpret and respond to sexual conductat work. Journal of Contemporary Ethnography, 39(3),297–327. doi:10.1177/0891241609341827.Derks, B., Van Laar, C., & Ellemers, N. (2007). The beneficialeffects of social identity protection on theperformance motivation of members of devaluedgroups. Social Issues and Policy Review, 1(1), 217–56.doi:10.1111/j.1751-2409.2007.00008.x.Devine, P. G., Forscher, P. S., Austin, A. J., & Cox, W. T. L.(2012). Long-term reduction in implicit race bias: A prejudicehabit-breaking intervention. Journal of ExperimentalSocial Psychology, 48(6), 1267–78. doi:10.1016/j.jesp.2012.06.003.

AAUW 131Diekman, A. B. (2007). Negotiating the double bind:Interpersonal and instrumental evaluations of dominance.Sex Roles, 56(9–10), 551–61. doi:10.1007/s11199-007-9198-0.Diekman, A. B., Brown, E. R., Johnston, A. M., & Clark, E.K. (2010). Seeking congruity between goals and roles: Anew look at why women opt out of science, technology,engineering, and mathematics careers. Psychological Science,21(8), 1051–57. doi:10.1177/0956797610377342.Diekman, A. B., Clark, E. K., Johnston, A. M., Brown, E. R., &Steinberg, M. (2011). Malleability in communal goals andbeliefs influences attraction to STEM careers: Evidencefor a goal congruity perspective. Journal of Personality andSocial Psychology, 101(5), 902–18. doi:10.1037/a0025199.Diekman, A. B., & Steinberg, M. (2013). Navigating socialroles in pursuit of important goals: A communalgoal congruity account of STEM pursuits. Social andPersonality Psychology Compass, 7(7), 487–501. doi:10.1111/spc3.12042.Diekman, A. B., Weisgram, E. S., & Belanger, A. L. (2015). Newroutes to recruiting and retaining women in STEM: Policyimplications of a communal goal congruity perspective.Social Issues and Policy Review, 9(1), 52–88. doi:10.1111/sipr.12010.Dodds, Z., Libeskind-Hadas, R., Alvarado, C., & Kuenning,G. (2008). Evaluating a breadth-first CS 1 for scientists.SIGCSE ’08: Proceedings of the 39th SIGCSEtechnical symposium on computer science education, 266–70.doi:10.1145/1352135.1352229.Dovidio, J. F. (2001). On the nature of contemporary prejudice:The third wave. Journal of Social Issues, 57(4), 829–49.doi:10.1111/0022-4537.00244.Dovidio, J. F., Hewstone, M., Glick, P., & Esses, V. M. (Eds.).(2010). The SAGE handbook of prejudice, stereotyping, anddiscrimination. Thousand Oaks, CA: SAGE Publications.Drury, B. J., Siy, J. O., & Cheryan, S. (2011). When do femalerole models benefit women? The importance of differentiatingrecruitment from retention in STEM.Psychological Inquiry, 22(4), 265–69. doi:10.1080/1047840X.2011.620935.Dweck, C. (2007). Mindset: The new psychology of success. NewYork, NY: Ballentine Books.Eagan, K., Stolzenberg, E. B., Ramirez, J. J., Aragon, M. C.,Suchard, M. R., & Hurtado, S. (2014). The American freshman:National norms fall 2014. Los Angeles, CA: UCLAHigher Education Research Institute.Eagly, A. H. (1987). Sex differences in social behavior: A social-roleinterpretation. Hillsdale, NJ: Erlbaum.Eagly, A. H., & Diekman, A. B. (2003). The malleability ofsex differences in response to changing social roles. In L.G. Aspinwall & U. M. Staudinger (Eds.), A psychology ofhuman strengths: Fundamental questions and future directionsfor a positive psychology (Ch. 8, pp. 103–15). Washington,DC: American Psychological Association.———. (2012). Prejudice in context departs from attitudestoward groups. Behavioral and Brain Sciences, 35(6), 431–2.doi:10.1017/S0140525X12001185.Eagly, A. H., & Wood, W. (2012). Social role theory. In P. A.M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.),Handbook of theories in social psychology (Vol. 2, pp. 458–76).London, England: SAGE.Eccles, J. S. (1994). Understanding women’s educational andoccupational choices: Applying the Eccles et al. modelof achievement-related choices. Psychology of WomenQuarterly, 18(4), 585–609. doi:10.1111/j.1471-6402.1994.tb01049.x.———. (2007). Where are all the women? Gender differencesin participation in physical science and engineering. In S.J. Ceci & W. M. Williams (Eds.), Why aren’t more womenin science? Top researchers debate the evidence (pp. 199–210).Washington, DC: American Psychological Association.———. (2009). Who am I and what am I going to do withmy life? Personal and collective identities as motivatorsof action. Educational Psychologist, 44(2), 78–89.doi:10.1080/00461520902832368.———. (2011a). Gender and occupational choices in the sciences.Annual International Collegium Lecture at the HelsinkiCollegium for Advanced Studies, Helsinki, Finland.———. (2011b). Gendered educational and occupationalchoices: Applying the Eccles et al. modelof achievement-related choices. InternationalJournal of Behavioral Development, 35(3), 195–201.doi:10.1177/0165025411398185.Eccles (Parsons), J. S., Adler, T. E., Futterman, R., Goff, S.B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983).Expectancies, values, and academic behaviors. In J. T.Spence (Ed.), Achievement and achievement motives:Psychological and sociological approaches (pp. 75–146). SanFrancisco, CA: W. H. Freeman.Else-Quest, N. M., Mineo, C. C., & Higgins, A. (2013). Mathand science attitudes and achievement at the intersectionof gender and ethnicity. Psychology of Women Quarterly,37(3), 293–309. doi:10.1177/0361684313480694.England, P. (2010). The gender revolution: Unevenand stalled. Gender and Society, 24(2), 149–66.doi:10.1177/0891243210361475.Ensmenger, N. L. (2010a). Making programming masculine.In T. J. Misa (Ed.), Gender codes: Why women are leavingcomputing (115–42). Hoboken, NJ: Wiley.———. (2010b).The computer boys take over: Computers, programmers,and the politics of technical expertise. Cambridge, MA:MIT Press.Evans, C. D., & Diekman, A. B. (2009). On motivated roleselection: Gender beliefs, distant goals, and careerinterest. Psychology of Women Quarterly, 33(2), 235–49.doi:10.1111/j.1471-6402.2009.01493.x.Facebook. (2014). Building a more diverse Facebook, byM. Gibbons. newsroom.fb.com/news/2014/06/building-a-more-diverse-facebook.

132SOLVING THE EQUATIONFaulkner, W. (2007). “Nuts and bolts and people”: Gendertroubledengineering identities. Social Studies of Science,37(3), 331–56. doi:10.1177/0306312706072175.———. (2009a). Doing gender in engineering workplace cultures.I. Observations from the field. Engineering Studies,1(1), 3–18. doi:10.1080/19378620902721322.———. (2009b). Doing gender in engineering workplacecultures. II. Gender in/authenticity and the in/visibilityparadox. Engineering Studies, 1(3), 169–89.doi:10.1080/19378620903225059.Fiske, S. T. (2012). Managing ambivalent prejudices: Smartbut-coldand warm-but-dumb stereotypes. Annals of theAmerican Academy of Political and Social Science, 639(1),33–48. doi:10.1177/0002716211418444.Fiske, S. T., & Taylor, S. E. (1991). Social cognition. New York,NY: McGraw-Hill.Fletcher, J. K. (1999). Disappearing acts: Gender, power, and relationalpractice at work. Cambridge, MA: MIT Press.Fouad, N. A., Singh, R., Fitzpatrick, M. E., & Liu, J. P. (2012).Stemming the tide: Why women leave engineering. Universityof Wisconsin, Milwaukee.Fox, M. F. (2010). Women and men faculty in academic scienceand engineering: Social-organizational indicatorsand implications. American Behavioral Scientist, 53(7),997–1012. doi:10.1177/0002764209356234.Frehill, L. M. (2004). The gendered construction ofthe engineering profession in the United States,1893–1920. Men and Masculinities, 6(4), 83–403.doi:10.1177/1097184X03260963.———. (2010, January). Satisfaction: Why do people give upengineering? Surveys of men and women engineers tell anunexpected story. Mechanical Engineering, 38–41.———. (2011). Engineering salaries. NACME Research andPolicy Brief, 1(6).———. (2012). Gender and career outcomes of U.S. engineers.International Journal of Gender, Science and Technology, 4(2),148–66.Frehill, L. M., & Cohoon, J. M. (2015). Gender and computing.In W. Pearson, Jr., L. M. Frehill, & C. L. McNeely (Eds.),Advancing women in science: An international perspective.New York, NY: Springer.Fullinwider, R. (2014, Winter). Affirmative action. Stanfordencyclopedia of philosophy.Glass, J. L., Sassler, S., Levitte, Y., & Michelmore, K. M. (2013).What’s so special about STEM? A comparison of women’sretention in STEM and professional occupations. SocialForces, 92(2), 723–56. doi:10.1093/sf/sot092.Glick, P., & Fiske, S. T. (1996). The Ambivalent SexismInventory: Differentiating hostile and benevolent sexism.Journal of Personality and Social Psychology, 70(3), 491–512.doi:10.1037/0022-3514.70.3.491.Goldin, C. (2014). A grand gender convergence: Its lastchapter. American Economic Review, 104(4), 1091–119.doi:10.1257/aer.104.4.1091.Goldin, C., & Rouse, C. (2000). Orchestrating impartiality: Theimpact of “blind” auditions on female musicians. AmericanEconomic Review, 90(4), 715–41. doi:10.1257/aer.90.4.715.Good, C., Aronson, J., & Harder, J. A. (2008). Problems inthe pipeline: Stereotype threat and women’s achievementin high-level math courses. Journal of AppliedDevelopmental Psychology, 29(1), 17–28. doi:10.1016/j.appdev.2007.10.004.Good, C., Rattan, A., & Dweck, C. S. (2012). Why do womenopt out? Sense of belonging and women’s representationin mathematics. Journal of Personality and Social Psychology,102(4), 700–17. doi:10.1037/a0026659.Good, J. J., & Rudman, L. A. (2010). When female applicantsmeet sexist interviewers: The costs of being atarget of benevolent sexism. Sex Roles, 62(7–8), 481–93.doi:10.1007/s11199-009-9685-6.Google. (2014a). Making Google a workplace for everyone.www.google.com/diversity/at-google.html.———. (2014b). Women who choose computer science—what reallymatters: The critical role of encouragement and exposure.Grant, A. M. (2007). Relational job design and the motivationto make a prosocial difference. Academy ofManagement Review, 32(2), 393–417. doi:10.5465/AMR.2007.24351328.———. (2013). Give and take: A revolutionary approach to success.New York, NY: Viking.Grant, A. M., Campbell, E. M., Chen, G., Cottone, K., Lapedis,D., & Lee, K. (2007). Impact and the art of motivationmaintenance: The effects of contact with beneficiarieson persistence behavior. Organizational Behavior andHuman Decision Processes, 103(1), 53–67. doi:10.1016/j.obhdp.2006.05.004.Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond,K. L., Iezzoni, L. I., & Banaji, M. R. (2007). Implicitbias among physicians and its prediction of thrombolysisdecisions for black and white patients. Journal ofGeneral Internal Medicine, 22(9), 1231–38. doi:10.1007/s11606-007-0258-5.Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K.(1998). Measuring individual differences in implicitcognition: The Implicit Association Test. Journalof Personality and Social Psychology, 74(6), 1464–80.doi:10.1037/0022-3514.74.6.1464.Greenwald, A. G. & Pettigrew, T. F. (2014). With malice towardnone and charity for some: Ingroup favoritism enablesdiscrimination. American Psychologist, 69(7), 669–84.doi:10.1037/a0036056.Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji,M. R. (2009). Understanding and using the ImplicitAssociation Test: III. Meta-analysis of predictive validity.Journal of Personality and Social Psychology, 97(1), 17–41.doi:10.1037/a0015575.Gunderson, E. A., Ramirez, G., Levine, S. C., & Beilock, S. L.(2012). The role of parents and teachers in the developmentof gender-related math attitudes. Sex Roles, 66(3–4),153–66. doi:10.1007/s11199-011-9996-2.

AAUW 133Gutiérrez, A. S., & Unzueta, M. M. (2010). The effect of interethnicideologies on the likeability of stereotypic vs. counterstereotypicminority targets. Journal of ExperimentalSocial Psychology, 46(5), 775–84. doi:10.1016/j.jesp.2010.03.010.Guzdial, M. (2014, September 2). The most gender-balancedcomputing program in the USA: Computational media atGeorgia Tech [Blog post]. computinged.wordpress.com.Haddon, L. (1992). Explaining ICT consumption: The case ofthe home computer. In R. Silverstone & E. Hirsh (Eds.)Consuming technologies: Media and information in domesticspaces (pp. 82–96). London, England: Routledge.Hafer, C. L., & Bègue, L. (2005). Experimental researchon just-world theory: Problems, developments, andfuture challenges. Psychological Bulletin, 131(1), 128–67.doi:10.1037/0033-2909.131.1.128.Hall, W., Schmader, T., & Croft, E. (in press). Engineeringexchanges: Women’s daily experience of social identitythreat in engineering cue burnout. Social Psychology andPersonality Science.Harrison, D. A., Kravitz, D. A., Mayer, D. M., Leslie, L.M., & Lev-Arey, D. (2006). Understanding attitudestoward affirmative action programs in employment:Summary and meta-analysis of 35 years ofresearch. Journal of Applied Psychology, 91(5), 1013–36.doi:10.1037/0021-9010.91.5.1013.Harrison, J., & Klotz, L. (2010). Women as sustainability leadersin engineering: Evidence from industry and academia.International Journal of Engineering Education, 26(3),727–34.Hassan, S., & Hatmaker, D. M. (2014). Leadership and performanceof public employees: Effects of the quality andcharacteristics of manager-employee relationships. Journalof Public Administration Research and Theory. doi:10.1093/jopart/muu002.Hatmaker, D. M. (2013). Engineering identity: Gender andprofessional identity negotiation among women engineers.Gender, Work, and Organization, 20(4), 382–96.doi:10.1111/j.1468-0432.2012.00589.x.Hebl, M. R., Foster, J. B., Mannix, L. M., & Dovidio, J.F. (2002). Formal and interpersonal discrimination:A field study of bias toward homosexual applicants.Personality and Social Psychology Bulletin, 28(6), 815–25.doi:10.1177/0146167202289010.Heilman, M. E. (2001). Description and prescription: Howgender stereo types prevent women’s ascent up the organizationalladder. Journal of Social Issues, 57(4), 657–74.doi:10.1111/0022-4537.00234.———. (2012). Gender stereotypes and workplace bias. Researchin Organizational Behavior, 32, 113–35. doi:10.1016/j.riob.2012.11.003.Heilman, M. E., & Chen, J. J. (2005). Same behavior, differentconsequences: Reactions to men’s and women’s altruisticcitizenship behavior. Journal of Applied Psychology, 90(3),431–41. doi:10.1037/0021-9010.90.3.431.Heilman, M. E., & Eagly, A. H. (2008). Gender stereotypes arealive, well, and busy producing workplace discrimination.Industrial and Organizational Psychology, 1(4), 393–98.doi:10.1111/j.1754-9434.2008.00072.x.Heilman, M. E., & Okimoto, T. G. (2007). Why are womenpenalized for success at male tasks? The implied communalitydeficit. Journal of Applied Psychology, 92(1), 81–92.doi:10.1037/0021-9010.92.1.81.Heilman, M. E., Wallen, A. S., Fuchs, D., & Tamkins, M. M.(2004). Penalties for success: Reaction to women whosucceed in male gender-typed tasks. Journal of AppliedPsychology, 89(3), 416–27.Hekman, D. R., & Foo, M.-D. (2014, August 4). Does valuingdiversity result in worse performance ratings for minorityand female leaders? Paper presented at Academy ofManagement annual meeting, Philadelphia, PA.Hess, C., Gault, B., & Yi, Y. (2013, November). Acceleratingchange for women faculty of color in STEM: Policy, action,and collaboration. Washington, DC: Institute for Women’sPolicy Research. www.iwpr.org/publications/pubs/accelerating-change-for-women-faculty-of-color-in-stem-policyaction-and-collaboration.Hess, K. P. (2013). Investigation of nonverbal discriminationagainst women in simulated initial job interviews.Journal of Applied Social Psychology, 43(3), 544–55.doi:10.1111/j.1559-1816.2013.01034.x.Hess, T. M., Auman, C., Colcombe, S. J., & Rahhal, T. A.(2003). The impact of stereotype threat on age differencesin memory performance. Journals of Gerontology B:Psychological Sciences, 58(1), 3–11.Hewlett, S. A., Buck Luce, C., Servon, L. J., Sherbin, L., Shiller,P., Sosnovich, E., & Sumberg, K. (2008). The Athena factor:Reversing the brain drain in science, engineering, and technology(Harvard Business Review Research Report). Boston,MA: Harvard Business Publishing.Hewlett, S. A., Sherbin, L., Dieudonné, F., Fargnoli, C., &Fredman, C. (2014). Athena factor 2.0: Accelerating femaletalent in science, engineering, and technology. New York, NY:Center for Talent Innovation.Hively, K., & El-Alayli, A. (2014). “You throw like a girl”: Theeffect of stereotype threat on women’s athletic performanceand gender stereotypes. Psychology of Sports and Exercise,15(1), 48–55. doi:10.1016/j.psychsport.2013.09.001.Holleran, S. E., Whitehead, J., Schmader, T., & Mehl, M. R.(2011). Talking shop and shooting the breeze: A study ofworkplace conversation and job disengagement amongSTEM faculty. Social Psychological and Personality Science,2(1), 65–71. doi:10.1177/1948550610379921.Holoien, D. S., & Fiske, S. T. (2013). Downplaying positiveimpressions: Compensation between warmth andcompetence in impression management. Journal ofExperimental Social Psychology, 49(1), 33–41. doi:10.1016/j.jesp.2012.09.001.Hugenberg, K., & Bodenhausen, G. V. (2003). Facingprejudice: Implicit prejudice and the perception of

134SOLVING THE EQUATIONfacial threat. Psychological Science, 14(6), 640–43.doi:10.1046/j.0956-7976.2003.psci_1478.x.Hunt, J. (2010). Why do women leave science and engineering?(NBER working paper 15853). Cambridge, MA: NationalBureau of Economic Research.Hunt, J., Garant, J.-P., Herman, H., & Munroe, D. J. (2012).Why don’t women patent? (NBER working paper no.17888). Cambridge, MA: National Bureau of EconomicResearch.Hunter, R. (2006). Discrimination in IT organisations. Labourand Industry, 16(3), 91–108. doi:10.1080/10301763.2006.10669332.Intel Corporation. (2014). Equal employment opportunity:2013 Employer information report for Intel Corp. www.intel.com/content/www/us/en/company-overview/equalemployment-opportunity-employer-info-report.html.Inzlicht, M., & Ben-Zeev, T. (2000). A threatening intellectualenvironment: Why females are susceptible toexperiencing problem-solving deficits in the presenceof men. Psychological Science, 11(5), 365–71.doi:10.1111/1467-9280.00272.———. (2003). Do high-achieving female studentsunderperform in private? The implication of threateningenvironments on intellectual processing.Journal of Educational Psychology, 95(4), 796–805.doi:10.1037/0022-0663.95.4.796.Isaac, C., Lee, B., & Carnes, M. (2009). Interventions thataffect gender bias in hiring: A systematic review.Academic Medicine, 84(10), 1440–46. doi:10.1097/ACM.0b013e3181b6ba00.Iskander, E. T. (2013). Gender disparity in engineering fields: Ananalysis of historic data, and school counselors’ knowledge,values and attitudes (Doctoral dissertation). University ofUtah.Iskander, E. T., Gore, P. A., Jr., Furse, C., & Bergerson, A.(2013). Gender differences in expressed interests inengineering-related fields: ACT 30-year data analysisidentified trends and suggested avenues to reversetrends. Journal of Career Assessment, 21(4), 599–613.doi:10.1177/1069072712475290.Johns, M., Schmader, T., & Martens, A. (2005). Knowing ishalf the battle: Teaching stereotype threat as a means ofimproving women’s math performance. Psychological Science,16(3), 175–79. doi:10.1111/j.0956-7976.2005.00799.x.Jones, M. G., Howe, A., & Rua, M. J. (2000). Gender differencesin students’ experiences, interests, and attitudes towardscience and scientists. Science Education, 84(2), 180–92.doi:10.1002/(SICI)1098-237X(200003)84:23.0.CO;2-X.Joshi, A. (2014). By whom and when is women’s expertiserecognized? The interactive effects of gender and educationin science and engineering teams. Administrative ScienceQuarterly, 59(2), 202–39. doi:10.1177/0001839214528331.Jozefowicz, D. M., Barber, B. L., & Eccles, J. S. (1993, March29). Adolescent work-related values and beliefs: Gender differencesand relation to occupational aspirations. Paper presentedat the biennial meeting of the Society for Research inChild Development, New Orleans, LA.Kalev, A., Dobbin, F., & Kelly, E. (2006). Best practices or bestguesses? Assessing the efficacy of corporate affirmativeaction and diversity policies. American Sociological Review,71(4), 589–617. doi:10.1177/000312240607100404.Kalokerinos, E. K., von Hippel, C., & Zacher, H. (2014).Is stereotype threat a useful construct for organizationalpsychology research and practice? Industrial andOrganizational Psychology, 7(3), 381–402. doi:10.1111/iops.12167.Kantamneni, N., & Fouad, N. (2011). Structure of vocationalinterests for diverse groups on the 2005 Strong InterestInventory. Journal of Vocational Behavior, 78(2), 193–201.doi:10.1016/j.jvb.2010.06.003.Kimmell, L. G., Miller, J. D., & Eccles, J. S. (2012). Do thepaths to STEMM professions differ by gender? PeabodyJournal of Education, 87(1), 92–113. doi:10.1080/0161956X.2012.642276.King, E. B., Botsford, W., Hebl, M. R., Kazama, S., Dawson,J. F., & Perkins, A. (2012). Benevolent sexism at work:Gender differences in the distribution of challengingdevelopmental experiences. Journal of Management, 38(6),1835–66. doi:10.1177/0149206310365902.Koch, S. C., Konigorski, S., & Sieverding, M. (2014). Sexistbehavior undermines women’s performance in a job applicationsituation. Sex Roles, 70(3–4), 79–87. doi:10.1007/s11199-014-0342-3.Konrad, A. M., Ritchie, J. E., Jr., Lieb, P., & Corrigall, E. (2000).Sex differences and similarities in job attribute preferences:A meta-analysis. Psychological Bulletin, 126(4), 593–641.doi:10.1037/0033-2909.126.4.593.Koput, K. W., & Gutek, B. A. (2010). Gender stratification in theIT industry: Sex, status, and social capital. Northampton,MA: Edward Elgar.Kurtulus, F. A., & Tomaskovic-Devey, D. (2012, January). Dofemale top managers help women to advance? A panelstudy using EEO-1 records. Annals of the AmericanAcademy of Political and Social Science, 639, 173.Lai, C. K., Hoffman, K. M., & Nosek, B. A. (2013). Reducingimplicit prejudice. Social and Personality PsychologyCompass, 7(5), 315–30. doi:10.1111/spc3.12023.Landivar, L. C. (2013). The relationship between science andengineering education and employment in STEM occupations(American Community Survey Reports, ACS-23).Washington, DC: U.S. Census Bureau.Lane, K. A., Goh, J. X., & Driver-Linn, E. (2012). Implicit sciencestereotypes mediate the relationship between genderand academic participation. Sex Roles, 66(3–4), 220–34.doi:10.1007/s11199-011-0036-z.Lee, A. (2011). A comparison of postsecondary science, technology,engineering, and mathematics (STEM) enrollment forstudents with and without disabilities. Career Developmentand Transition for Exceptional Individuals, 34(2), 72–82.doi:10.1177/0885728810386591.

AAUW 135Legault, L., Al-Khindi, T., & Inzlicht, M. (2012). Preservingintegrity in the face of performance threat: Selfaffirmationenhances neurophysiological responsivenessto errors. Psychological Science, 23(12), 1455–60.doi:10.1177/0956797612448483.Lichtenstein, G., McCormick, A. C., Sheppard, S. D., & Puma,J. (2010). Comparing the undergraduate experience ofengineers to all other majors: Significant differences areprogrammatic. Journal of Engineering Education, 99(4),305–17. doi:10.1002/j.2168-9830.2010.tb01065.x.Light, J. S. (1999). When computers were women. Technologyand Culture, 40(3), 455–85.Logel, C., Walton, G. M., Spencer, S. J., Iserman, E. C., vonHippel, W., & Bell, A. E. (2009). Interacting with sexistmen triggers social identity threat among female engineers.Journal of Personality and Social Psychology, 96(6),1089–103. doi:10.1037/a0015703.London, B., Ahlqvist, S., Gonzalez, A., Glanton, K. V., &Thompson, G. A. (2014). The social and educational consequencesof identity-based rejection. Social Issues and PolicyReview, 8(1), 131–66. doi:10.1111/sipr.12004.London, B., Downey, G., Romero-Canyas, R., Rattan, A. &Tyson, D. (2012). Gender-based rejection sensitivity andacademic self-silencing in women. Journal of Personalityand Social Psychology, 102(5): 961–79.London, B., Rosenthal, L., Levy, S. R., & Lobel, M. (2011). Theinfluences of perceived identity compatibility and socialsupport on women in nontraditional fields during thecollege transition. Basic and Applied Social Psychology, 33(4),304–21. doi:10.1080/01973533.2011.614166.Lord, S. M., Ohland, M. W., Layton, R. A., & Orr, M. K.(2013, October). Student demographics and outcomesin electrical and mechanical engineering. In IEEEFrontiers in Education Conference, pp. 57–63. doi:10.1109/FIE.2013.6684788.Lubinski, D., & Benbow, C. P. (2006). Study of mathematicallyprecocious youth after 35 years: Uncovering antecedentsfor the development of math-science expertise.Perspectives on Psychological Science, 1(4), 316–45.doi:10.1111/j.1745-6916.2006.00019.x.Lucas, J. W., & Baxter, A. R. (2012). Power, influence, anddiversity in organizations. Annals of the AmericanAcademy of Political and Social Science, 639(1), 49–70.doi:10.1177/0002716211420231.Lynch, R. (2010). It’s funny because we think it’s true:Laughter is augmented by implicit preferences. Evolutionand Human Behavior, 31(2), 141–48. doi:10.1016/j.evolhumbehav.2009.07.003.Lyness, K. S., & Heilman, M. E. (2006). When fit is fundamental:Performance evaluations and promotions of upper-levelfemale and male managers. Journal of Applied Psychology,91(4), 777–85. doi:10.1037/0021-9010.91.4.777.Maltese, A. V. & Tai, R. H. (2011). Pipeline persistence:Examining the association of educational experiences withearned degrees in STEM among U.S. students. ScienceEducation, 95(5), 877–907. doi:10.1002/sce.20441.Manke, K. J., & Cohen, G. L. (2011). More than inspiration:Role models convey multiple and multifaceted messages.Psychological Inquiry, 22(4), 275–79. doi:10.1080/1047840X.2011.622254.Marchetti, C. E., Bailey, M. B., Baum, S. A., Mason, S. P., &Valentine, M. S. (2012, June). Perceived levels of facultyvalue, influence, and satisfaction by gender, rank, ethnicity,college, and department at a large private university. Paperpresented at the 2012 American Society for EngineeringEducation annual conference, San Antonio, TX.Margolis, J., & Fisher, A. (2002). Unlocking the clubhouse: Womenin computing. Cambridge, MA: MIT Press.Margolis, J., Fisher, A., & Miller, F. (2002). Caring about connections:Gender and computing. Pittsburgh, PA: CarnegieMellon University.Martens, A., Johns, M., Greenberg, J., & Schimel, J. (2006).Combating stereotype threat: The effect of selfaffirmationon women’s intellectual performance.Journal of Experimental Social Psychology, 42(2), 236–43.doi:10.1016/j.jesp.2005.04.010.Mason, M. A., & Goulden, M. (2002). Do babies matter? Theeffect of family formation on the lifelong careers of academicmen and women. Academe, 88(6), 21–7.Mateyka, P. J., Rapino, M. A., & Landivar, L. C. (2012). Homebasedworkers in the United States: 2010 (Current populationreports, P70-132). Washington, DC: U.S. CensusBureau.McConnell, A. R., & Liebold, J. M. (2001). Relations amongthe Implicit Association Test, discriminatory behavior, andexplicit measures of racial attitudes. Journal of ExperimentalSocial Psychology, 37, 435–42. doi:10.1006/jesp.2000.1470.McIlwee, J. S., & Robinson, J. G. (1992). Women in engineering:Gender, power, and workplace culture. Albany, NY: StateUniversity of New York Press.McLaughlin, H., Uggen, C., & Blackstone, A. (2012). Sexualharassment, workplace authority, and the paradox ofpower. American Sociological Review, 77(4), 625–47.doi:10.1177/0003122412451728.Milkman, K. L., Akinola, M., & Chugh, D. (2014, December13). What happens before? A field experiment exploring howpay and representation differentially shape bias on the pathwayinto organizations. doi:10.2139/ssrn.2063742.Miner-Rubino, K., & Cortina, L. M. (2007). Beyond targets:Consequences of vicarious exposure to misogynyat work. Journal of Applied Psychology, 92(5), 1254–69.doi:10.1037/0021-9010.92.5.1254.Misa, T. J. (2010). Gender codes: Lessons from history. In T. J.Misa (Ed.), Gender codes: Why women are leaving computing(pp. 251–64). Hoboken, NJ: Wiley.Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M.J., & Handelsman, J. (2012a). Science faculty’s subtle genderbiases favor male students. Proceedings of the NationalAcademy of Sciences, 109(41), 16474–79. doi:10.1073/pnas.1211286109.———. (2012b). Science faculty’s subtle gender biases favormale students: Supporting information. Proceedings of

136SOLVING THE EQUATIONthe National Academy of Sciences, 109(41). doi:10.1073/pnas.1211286109.Moss-Racusin, C. A., van der Toorn, J., Dovidio, J. F., Brescoll,V. L., Graham, M. J., & Handelsman, J. (2014). Scientificdiversity interventions. Science, 343(6171), 615–16.doi:10.1126/science.1245936.Murphy, M. C., Steele, C. M., & Gross, J. J. (2007). Signalingthreat: How situational cues affect women in math, science,and engineering settings. Psychological Science, 18(10),879–85.National Academy of Engineering. (2008). Changing theconversation: Messages for improving public understanding ofengineering. Washington, DC: National Academies Press.National Academy of Sciences. (2013). Seeking solutions:Maximizing American talent by advancing women of colorin academia: Summary of a conference. Washington, DC:National Academies Press.National Academy of Sciences, National Academy ofEngineering, and Institute of Medicine, Rising above theGathering Storm Committee. (2010). Rising above thegathering storm, revisited: Rapidly approaching category 5.Washington, D.C: National Academies Press.National Association of Colleges and Employers. (2014,September). Salary survey. Bethlemen, PA: Author.National Center for Women and Information Technology.(2012). Who invents IT? An analysis of women’s participationin information technology patenting, by C. Ashcraft & A.Breitzman. Boulder, CO: Author.———. (2014a). NCWIT scorecard: A report on the status ofwomen in information technology. Boulder, CO: Author.———. (2014b). What is the impact of gender diversity on technologybusiness performance? Research summary, by L. Barker,C. Mancha, & C. Ashcraft. Boulder, CO: Author.National Research Council. (2010). Gender differences at criticaltransitions in the careers of science, engineering, and mathematicsfaculty. Washington, DC: National Academies Press.National Science Board. (2008). Science and engineering indicators2008 (NSB 08-01 and NSB 08-01A). Arlington, VA:Author.———. (2014). Science and engineering indicators 2014 (NSB14-01). Arlington VA: Author.National Science Foundation, Division of Science ResourcesStatistics. (2013). Science and engineering degrees:1966–2010, Detailed statistical tables: Tables 11, 26, 29,33, 34, 37, 38, 46 (NSF 13-327). Arlington, VA: Author.www.nsf.gov/statistics/nsf13327.National Science Foundation, National Center for Science andEngineering Statistics. (2010a). National survey of collegegraduates, NSCG public. Scientists and engineers statisticaldata system (SESTAT).———. (2010b). National survey of recent college graduates,NSRCG public. Scientists and engineers statistical datasystem (SESTAT).———. (2013). Integrated postsecondary education data system(IPEDS) completions survey, 2012. Integrated science andengineering resources data system (WebCASPAR).———. (2014a). Integrated postsecondary education data system(IPEDS) completions survey, 2013. Integrated scienceand engineering resources data system (WebCASPAR).———. (2014b). Integrated postsecondary education datasystem (IPEDS) completions survey by race, 2013.Integrated science and engineering resources data system(WebCASPAR).———. (2014c). Women, minorities, and persons with disabilitiesin science and engineering: 2013 (Special report NSF13-304). Arlington, VA: Author. www.nsf.gov/statistics/wmpd.Nguyen, H. H., & Ryan, A. M. (2008). Does stereotype threataffect test performance of minorities and women? Ameta-analysis of experimental evidence. Journal of AppliedPsychology, 93(6), 1314–34. doi:10.1037/a0012702.Nishii, L. H. (2013). The benefits of climate for inclusion forgender-diverse groups. Academy of Management Journal,56(6), 1754–74. doi:10.5465/amj.2009.0823.Nosek, B. A. (2007). Implicit-explicit relations. CurrentDirections in Psychological Science, 16(2), 65–69.doi:10.1111/j.1467-8721.2007.00477.x.Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002a).Math=male, me=female, therefore math≠me. Journalof Personality and Social Psychology, 83(1), 44–59.doi:10.1037//0022-3514.83.1.44.————. (2002b). Harvesting implicit group attitudesand beliefs from a demonstration web site. GroupDynamics: Theory, Research, and Practice, 6(1), 101–15.doi:10.1037/1089-2699.6.1.101.Nosek, B. A., & Smyth, F. L. (2007). A multitrait-multimethodvalidation of the Implicit Association Test:Implicit and explicit attitudes are related but distinctconstructs. Experimental Psychology, 54(1), 14–29.doi:10.1027/1618-3169.54.1.14.————. (2011). Implicit social cognitions predict sexdifferences in math engagement and achievement.American Educational Research Journal, 48(5), 1125–56.doi:10.3102/0002831211410683.Ohland, M. W., Sheppard, S. D., Lichtenstein, G., Eris,O., Chachra, D., & Layton, R. A. (2008). Persistence,engagement, and migration in engineering programs.Journal of Engineering Education, 97(3), 259–78.doi:10.1002/j.2168-9830.2008.tb00978.x.O’Leary-Kelly, A. M., Bowes-Sperry, L., Bates, C. A., &Lean, E. R. (2009). Sexual harassment at work: A decade(plus) of progress. Journal of Management, 35(3), 503–36.doi:10.1177/0149206308330555.Ong, M. (2011). The status of women of color in computerscience. Communications of the ACM, 54(7), 32–34.doi:10.1145/1965724.1965737.Ong, M., Wright, C., Espinosa, L. L., & Orfield, G., (2011).Inside the double bind: A synthesis of empirical researchon undergraduate and graduate women of color in science,technology, engineering, and mathematics. HarvardEducational Review, 81(2), 172–208.

AAUW 137Padavic, I., & Ely, R. J. (2013, March 1). The work-family narrativeas a social defense. Paper presented at the Genderand Work: Challenging Conventional Wisdom researchsymposium, Harvard Business School.Page, S. E. (2007). The difference: How the power of diversity createsbetter groups, firms, schools, and societies. Princeton, NJ:Princeton University Press.Parkin, S. (2014, October 17). Gamergate: A scandal erupts inthe video-game community. New Yorker.Patten, E. & Parker, K. (2012). A gender reversal on careeraspirations: Young women now top young men in valuing ahigh-paying career (Pew Social & Demographic Trends).Washington, DC: Pew Research Center.Pawley, A. L. (2012). What counts as “engineering”: Toward aredefinition. In C. Baillie, A. L. Pawley, & D. Riley (Eds.),Engineering and social justice: In the university and beyond(Ch. 3). West Lafayette, IN: Purdue University Press.Phelan, J. E., Moss-Racusin, C. A., & Rudman, L. A.(2008). Competent yet out in the cold: Shiftingcriteria for hiring reflect backlash toward agenticwomen. Psychology of Women Quarterly, 32(4), 406–13.doi:10.1111/j.1471-6402.2008.00454.x.Phillips, K. W., Liljenquist, K. A., & Neale, M. A. (2009).Is the pain worth the gain? The advantages and liabilitiesof agreeing with socially distinct newcomers.Personality and Social Psychology Bulletin, 35(3), 336–50.doi:10.1177/0146167208328062.Pierce, C. M. (1970). Offensive mechanisms. In F. B. Barbour(Ed.), The black seventies (pp. 265–82). Boston, MA: PorterSargent.Plaut, V. C., Thomas, K. M., & Goren, M. J. (2009).Is multiculturalism or color blindness better forminorities? Psychological Science, 20(4), 444–46.doi:10.1111/j.1467-9280.2009.02318.x.Pöhlmann, K. (2001). Agency- and communion-orientation inlife goals: Impacts on goal pursuit strategies and psychologicalwell-being. In P. Schmuck & K. M. Sheldon (Eds.),Life goals and well-being: Towards a positive psychology ofhuman striving (pp. 68–84). Kirkland, WA: Hogrefe &Huber.Prentice, D. A., & Carranza, E. (2002). What women andmen should be, shouldn’t be, are allowed to be, and don’thave to be: The contents of prescriptive gender stereotypes.Psychology of Women Quarterly, 26(4), 269–81.doi:10.1111/1471-6402.t01-1-00066.Prescott, B. T., & Bransberger, P. (2012). Knocking on the collegedoor: Projections of high school graduates (8th ed.).Boulder, CO: Western Interstate Commission for HigherEducation.Preston, A. E. (2004). Leaving science: Occupational exit fromscientific careers. New York, NY: Russell Sage Foundation.Purdie-Vaughns, V., Steele, C. M., Davies, P. G., Ditlmann,R., & Crosby, J. R. (2008). Social identity contingencies:How diversity cues signal threat or safety forAfrican Americans in mainstream institutions. Journalof Personality and Social Psychology, 94(4), 615–30.doi:10.1037/0022-3514.94.4.615.Quesenberry, J. L., & Trauth, E. M. (2012). The (dis)placementof women in the IT workforce: An investigationof individual career values and organizational interventions.Information Systems Journal, 22(6), 457–73.doi:10.1111/j.1365-2575.2012.00416.x.Reuben, E., Sapienza, P., & Zingales, L. (2014a). How stereotypesimpair women’s careers in science. Proceedingsof the National Academy of Sciences, 111(12), 4403–08.doi:10.1073/pnas.1314788111.———. (2014b). How stereotypes impair women’s careers inscience: Supplementary information appendix. Proceedingsof the National Academy of Sciences, 111(12). doi:10.1073/pnas.1314788111.Rhoton, L. A. (2011). Distancing as a gendered barrier:Understanding women scientists’ genderpractices. Gender and Society, 25(6), 696–716.doi:10.1177/0891243211422717.Richman, L. S., vanDellen, M. & Wood, W. (2011). Howwomen cope: Being a numerical minority in a male-dominatedprofession. Journal of Social Issues, 67(3), 492–509.doi:10.1111/j.1540-4560.2011.01711.x.Ridgeway, C. L. (1982). Status in groups: The importance ofmotivation. American Sociological Review, 47(1), 76–88.———. (2001). Gender, status, and leadership. Journal of SocialIssues, 57(4), 637–55. doi:10.1111/0022-4537.00233.———. (2009). Framed before we know it: How gendershapes social relations. Gender and Society, 23(2), 145–60.doi:10.1177/0891243208330313.Riegle-Crumb, C., King, B., Grodsky, E., & Muller, C. (2012).The more things change, the more they stay the same?Prior achievement fails to explain gender inequalityin entry into STEM college majors over time.American Educational Research Journal, 49(6), 1048–73.doi:10.3102/0002831211435229.Roberge, M.-E., & van Dick, R. (2010). Recognizing the benefitsof diversity: When and how does diversity increasegroup performance? Human Resources Management Review,20(4), 295–308. doi:10.1016/j.hrmr.2009.09.002.Roberts, E. S. (1999). Conserving the seed corn: Reflections onthe academic hiring crisis. ACM SIGSCE Bulletin, 31(4),4–9. doi:10.1145/349522.349363.———. (2011). Meeting the challenges of rising enrollments.ACM Inroads, 2(3), 4–6.Ross, M. C. (2012, June). PK–12 counselors’ knowledge, attitudes,and behaviors related to gender and STEM. Paper presentedat American Society for Engineering Education annualconference, San Antonio, TX.Rosser, S. V. (2012). Breaking into the lab: Engineering progress forwomen in science. New York University Press.Roth, W. D., & Sonnert, G. (2011). The costs and benefits of“red tape”: Anti-bureaucratic structure and gender inequityin a science research organization. Social Studies of Science,41(3), 385–409. doi:10.1177/0306312710391494.

138SOLVING THE EQUATIONRothgerber, H., & Wolsiefer, K. (2014). A naturalistic studyof stereotype threat in young female chess players.Group Processes and Intergroup Relations, 17(1), 79–90.doi:10.1177/1368430213490212.Rowe, M. P. (1990). Barriers to equality: The power ofsubtle discrimination to maintain unequal opportunity.Employee Responsibilities and Rights Journal, 3(2), 153–63.doi:10.1007/BF01388340.Rowe, M.P. (2008). Micro-affirmations and micro-inequities.Journal of the International Ombudsman Association, 1(1),45–48.Rudman, L. A., & Glick, P. (2001). Prescriptive gender stereotypesand backlash toward agentic woman. Journal of SocialIssues, 57(4), 743–62. doi:10.1111/0022-4537.00239.Rudman, L. A., Moss-Racusin, C. A., Phelan, J. E., & Nauts, S.(2012). Status incongruity and backlash effects: Defendingthe gender hierarchy motivates prejudice against femaleleaders. Journal of Experimental Social Psychology, 48,165–79.Rudman, L. A., & Phelan, J. E. (2008). Backlash effects fordisconfirming gender stereotypes in organizations. Researchin Organizational Behavior, 28, 61–79. doi:10.1016/j.riob.2008.04.003.Sadler, P. M., Sonnert, G., Hazari, Z., & Tai, R. (2012).Stability and volatility of STEM career interest in highschool: A gender study. Science Education, 96(3), 411–27.doi:10.1002/sce.21007.Sandrin, S., & Borror, C. M. (2013, June). Student perceptions andinterest in engineering: Effects of gender, race/ethnicity, andgrade level. Paper presented at the 120th ASEE AnnualConference, Atlanta, GA.Schiebinger, L., & Schraudner, M. (2011). Interdisciplinaryapproaches to achieving gendered innovations inscience, medicine, and engineering. InterdisciplinaryScience Reviews, 36(2), 154–67. doi:10.1179/030801811X13013181961518.Schlombs, C. (2010). A gendered job carousel: Employmenteffects of computer automation. In T. J. Misa (Ed.), Gendercodes: Why women are leaving computing (pp. 75–94).Hoboken, NJ: Wiley.Schmader, T. (2010). Stereotype threat deconstructed.Current Directions in Psychological Science, 19(1), 14–18.doi:10.1177/0963721409359292.———. (2013). The biases that bind us: How stereotypesconstrain how we think and whom we become. In R. J.Ely & A. J. C. Cuddy (Eds.), Gender and work: Challengingconventional wisdom. Cambridge, MA: Harvard BusinessSchool.Schmader, T., & Croft, A. (2011). How stereotypes stifle performancepotential. Social and Personality Psychology Compass,5(10), 792–806. doi:10.1111/j.1751-9004.2011.00390.x.Schmader, T., Forbes, C. E., Zhang, S., & Mendes, W. B. (2009).A metacognitive perspective on the cognitive deficitsexperienced in intellectually threatening environments.Personality and Social Psychology Bulletin, 35(5), 584–96.doi:10.1177/0146167208330450.Schmader, T., & Johns, M. (2003). Converging evidence thatstereotype threat reduces working memory capacity.Journal of Personality and Social Psychology, 85(3), 440–52.doi:10.1037/0022-3514.85.3.440.Schmader, T., Johns, M., & Forbes, C. (2008). An integratedprocess model of stereotype threat effects onperformance. Psychological Review, 115(2), 336–56.doi:10.1037/0033-295X.115.2.336.Schwartz, S. H. & Bardi, A. (2001). Value hierarchies acrosscultures: Taking a similarities perspective. Journal of Cross-Cultural Psychology, 32(3), 268–90. doi:10.1177/0022022101032003002.Servon, L. J., & Visser, M. A. (2011). Progress hindered:The retention and advancement of women in science,engineering and technology careers. HumanResource Management Journal, 21(3), 272–84.doi:10.1111/j.1748-8583.2010.00152.x.Settles, I. H. (2004). When multiple identities interfere: Therole of identity centrality. Personality and Social PsychologyBulletin, 30(4), 487–500. doi:10.1177/0146167203261885.Settles, I. H., Cortina, L. M., Buchanan, N. T., & Miner,K. N. (2013). Derogation, discrimination, and (dis)satisfaction with jobs in science: A gendered analysis.Psychology of Women Quarterly, 37(2), 179–91.doi:10.1177/0361684312468727.Settles, I. H., Cortina, L. M., Malley, J., & Stewart, A. J. (2006).The climate for women in academic science: The good, thebad, and the changeable. Psychology of Women Quarterly,30(1), 47–58. doi:10.1111/j.1471-6402.2006.00261.x.Settles, I. H., Cortina, L. M., Stewart, A. J., & Malley, J. (2007).Voice matters: Buffering the impact of a negative climatefor women in science. Psychology of Women Quarterly, 31(3),270–81. doi:10.1111/j.1471-6402.2007.00370.x.Sevo, R. (2009). Literature overview: The talent crisis in scienceand engineering. College Station, PA: SWE-AWE.Shackelford, S., Wood, W., & Worchel, S. (1996). Behavioralstyles and the influence of women in mixed-sex groups.Social Psychology Quarterly, 59(3), 284–93.Shnabel, N., Purdie-Vaughns, V., Cook, J. E., Garcia, J.,& Cohen, G. L. (2013). Demystifying values-affirmationinterventions: Writing about social belongingis a key to buffering against identity threat.Personality and Social Psychology Bulletin, 39(5), 663–76.doi:10.1177/0146167213480816.Simard, C., Henderson, A. D., Gilmartin, S. K., Schiebinger,L., & Whitney, T. (2008). Climbing the technical ladder:Obstacles and solutions for mid-level women in technology.Anita Borg Institute for Women and Technology andthe Clayman Institute for Gender Research, StanfordUniversity.Singh, R., Fouad, N. A., Fitzpatrick, M. E., Liu, J. P., Cappaert,K. J., & Figuereido, C. (2013). Stemming the tide:Predicting women engineers’ intention to leave. Journalof Vocational Behavior, 83(3), 281–94. doi:10.1016/j.jvb.2013.05.007.

AAUW 139Smeding, A. (2012). Women in science, technology, engineering,and mathematics (STEM): An investigation of theirimplicit gender stereotypes and stereotypes’ connectednessto math performance. Sex Roles, 67(11–12), 617–29.doi:10.1007/s11199-012-0209-4.Smith, J. L., Lewis, K. L., Hawthorne, L., & Hodges, S. D.(2013). When trying hard isn’t natural: Women’s belongingwith and motivation for male-dominated STEMfields as a function of effort expenditure concerns.Personality and Social Psychology Bulletin, 39(2), 131–43.doi:10.1177/0146167212468332.Smith, L. (2013). Working hard with gender: Gendered labourfor women in male dominated occupations of manualtrades and information technology (IT). Equality,Diversity, and Inclusion, 32(6), 592–603. doi:10.1108/EDI-12-2012-0116.Smyth, F. L., Greenwald, A. G., & Nosek, B. A. (2015). Onthe gender-science stereotypes held by scientists: Explicitaccord with gender-ratios, implicit accord with scientificself-concept. Manuscript submitted for publication in S.J. Ceci, W. M. Williams, & S. Kahn (Eds.), Frontiers inpsychology, Underrepresentation of women in science: internationaland cross-disciplinary evidence and debate.Smyth, F. L., Guilford, W. H., & Nosek, B. A. (2011). Firstyear engineering students are strikingly impoverished in theirself-concept as professional engineers. Washington, DC:American Society for Engineering Education.Snyder, T. D., & Dillow, S. A. (2013). Digest of educationstatistics 2012 (NCES 2014-015). Washington, DC: U.S.Department of Education, National Center for EducationStatistics.Society of Women Engineers. (2006). Attitudes and experiencesof engineering alumni, by Harris Interactive [UnpublishedPowerPoint presentation].Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotypethreat and women’s math performance. Journal ofExperimental Social Psychology, 35(1), 4–28.Stage, S. (1997). Ellen Richards and the social significanceof the home economics movement. In S. Stage & V. B.Vincenti (Eds.), Rethinking home economics: Women andthe history of a profession (pp. 17–33). Ithaca, NY: CornellUniversity Press.Stainback, K., Ratliff, T. N., & Roscigno, V. J. (2011). Thecontext of workplace sex discrimination: Sex composition,workplace culture, and relative power. Social Forces, 89(4),1165–88. doi:10.1093/sf/89.4.1165.Steele, C. M. (1997). A threat in the air: How stereotypesshape intellectual identity and performance.American Psychologist, 52(6), 613–29.doi:10.1037/0003-066X.52.6.613.Steele, C. M., & Aronson, J. (1995). Stereotype threat andthe intellectual test performance of African Americans.Journal of Personality and Social Psychology, 69(5), 797–811.doi:10.1037/0022-3514.69.5.797.Steele, C. M., Spencer, S. J., & Aronson, J. (2002). Contendingwith group image: The psychology of stereotype and socialidentity threat. Advances in Experimental Social Psychology,34, 379–440. doi:10.1016/S0065-2601(02)80009-0.Stevens, F. G., Plaut, V. C., & Sanchez-Burks, J. (2008).Unlocking the benefits of diversity: All-inclusivemulticulturalism and positive organizational change.Journal of Applied Behavioral Science, 44(1), 116–33.doi:10.1177/0021886308314460.Stout, J. G., & Dasgupta, N. (2013). Mastering one’s destiny:Mastery goals promote challenge and success despite socialidentity threat. Personality and Social Psychology Bulletin,39(6), 748–62. doi:10.1177/0146167213481067.Stout, J. G., Dasgupta, N., Hunsinger, M., & MacManus, M.A. (2011). STEMing the tide: Using ingroup expertsto inoculate women’s self-concept in science, technology,engineering, and mathematics (STEM). Journalof Personality and Social Psychology, 100(2), 255–70.doi:10.1037/a0021385.Su, L. K. (2010). Quantification of diversity in engineeringhigher education in the United States. Journal of Womenand Minorities in Science and Engineering, 16(2), 161–75.doi:10.1615/JWomenMinorScienEng.v16.i2.50.Swim, J. K., Aikin, K. J., Hall, W. S., & Hunter, B. A. (1995).Sexism and racism: Old-fashioned and modern prejudices.Journal of Personality and Social Psychology, 68(2), 199–214.doi:10.1037//0022-3514.68.2.199.Taylor, V. J., & Walton, G. M. (2011). Stereotype threat underminesacademic learning. Personality and Social PsychologyBulletin, 37(8), 1055–67. doi:10.1177/0146167211406506.Teague, J. (2002). Women in computing: What brings them toit, what keeps them in it? ACM SIGCSE Bulletin, 34(2),147–58. doi:10.1145/543812.543849.Tonso, K. L. (2007). On the outskirts of engineering: Learningidentity, gender, and power via engineering practice.Rotterdam, The Netherlands: Sense Publishers.Trapnell, P. D., & Paulhus, D. L. (2012). Agentic and communalvalues: Their scope and measurement. Journal of PersonalityAssessment, 94(1), 39–52. doi:10.1080/00223891.2011.627968.Triana, M. del C., Miller, T. L., & Trzebiatowski, T. M. (2013).The double-edged nature of board gender diversity:Diversity, firm performance, and the power of womendirectors as predictors of strategic change. OrganizationScience, 25(2), 609–32. doi:10.1287/orsc.2013.0842.Twenge, J. M. (1997). Changes in masculine and feminine traitsover time: A meta-analysis. Sex Roles, 36(5/6), 305–25.Twitter Inc. (2014). Building a Twitter we can be proudof, by J. Van Huysse. blog.twitter.com/2014/building-a-twitter-we-can-be-proud-of.Tyler-Wood, T., Ellison, A., Lim, O., & Periathiruvadi, S.(2012). Bringing up girls in science (BUGS): The effectivenessof an afterschool environmental science programfor increasing female students’ interest in science careers.Journal of Science Education and Technology, 21(1), 46–55.doi:10.1007/s10956-011-9279-2.Tyson, W., & Borman, K. M. (2010). “We’ve all learned a lotof ways not to solve the problem”: Perceptions of science

140SOLVING THE EQUATIONand engineering pathways among tenured women faculty.Journal of Women and Minorities in Science and Engineering16(4), 275–91. doi:10.1615/JWomenMinorScienEng.v16.i4.10.Uhlmann, E. L., & Cohen, G. L. (2005). Constructed criteria:Redefining merit to justify discrimination. PsychologicalScience, 16(6), 474–80.———. (2007). “I think it, therefore it’s true”: Effects ofself-perceived objectivity on hiring discrimination.Organizational Behavior and Human Decision Processes,104(2), 207–23. doi:10.1016/j.obhdp.2007.07.001.Unzueta, M. M., Knowles, E. D., & Ho, G. C. (2012). Diversityis what you want it to be: How social-dominance motivesaffect construals of diversity. Psychological Science, 23(3),303–9. doi:10.1177/0956797611426727.U.S. Census Bureau. (1960, 1970, 1980, 1990, 2000). Census ofthe population. Washington, DC: Author.———. (1963). Table 2. Detailed occupation of employed persons,by sex, for the United States, urban and rural: 1960.In U.S. census of population: 1960, subject reports, occupationalcharacteristics, final report PC(2)-7a. Washington, DC: U.S.Government Printing Office.———. (2011a). 2006–2010 American community survey: EEO1w; Detailed census occupation by sex and race/ethnicity forworksite geography; Civilians employed at work 16 years andover. Generated by AAUW with American FactFinder atfactfinder2.census.gov.———. (2011b). 2006–2010 American community survey: EEO9w; Detailed census occupation by educational attainment (6),sex, and race/ethnicity for worksite geography, total population.Generated by AAUW with American FactFinder atfactfinder2.census.gov.———. (2014a). 2012 American community survey: Occupationby field of bachelor’s degree: 2012. Data retrieved fromWhere do college graduates work? A special focus on science,technology, engineering, and math. www.census.gov/dataviz/visualizations/stem/stem-html.———. (2014b). Income and poverty in the United States: 2013,by C. DeNavas-Walt & B. D. Proctor. (Current PopulationReports P60-249). Washington, DC: GovernmentPrinting Office.———. (2014c). 2013 American community survey: TablesB24122 and B24123. Generated with AmericanFactFinder at factfinder2.census.gov.———. (2014d). Annual estimates of the resident population by sex,age, race, and Hispanic origin for the United States and states:April 1, 2010, to July 1, 2013. Generated with AmericanFactFinder at factfinder2.census.gov.U.S. Department of Education, National Center for EducationStatistics. (2010). The integrated postsecondary educationdata system (IPEDS) classification and instructionalprograms: CIP 2010. Washington, DC: Author.———. (2011). The nation’s report card: America’s high school graduates:Results of the 2009 NAEP high school transcript study,by C. Nord, S. Roey, R. Perkins, M. Lyons, N. Lemanski, J.Brown, & J. Schuknecht (NCES 2011-462). Washington,DC: Government Printing Office.U.S. Department of Labor, Bureau of Labor Statistics.(2009, January). 2010 standard occupational classification.Washington, DC: Author.———. (2011). Household data annual averages [2010]. Table11. Employed persons by detailed occupation, sex, race,and Hispanic or Latino ethnicity. In Labor Force Statisticsfrom the Current Population Survey: Tables from Employmentand Earnings. www.bls.gov/cps/aa2010/cpsaat11.pdf.———. (2014a). 2013 household data annual averages Table 9:Employed persons by occupation, sex, and age. In Laborforce statistics from the current population survey. www.bls.gov/cps/cpsaat09.htm.———. (2014b). 2013 household data annual averages Table 11:Employed persons by detailed occupation, sex, race, andHispanic or Latino ethnicity. In Labor force statistics fromthe current population survey. www.bls.gov/cps/cpsaat11.htm.———. (2014c). 2013 household data annual averages Table39: Median weekly earnings of full-time wage and salaryworkers by detailed occupation and sex. In Labor forcestatistics from the current population survey. www.bls.gov/cps/cpsaat39.pdf.———. (2014d). Employment projections: Table 1.2 Employmentby detailed occupation, 2012 and projected 2022.———. (2014e). Occupational outlook handbook [2014–15]. www.bls.gov/ooh.U.S. Department of Labor, Office of Federal ContractCompliance Programs. (2002, January 4). Facts on executiveorder 11246—affirmative action. www.dol.gov/ofccp/regs/compliance/aa.htm.Valla, J. M., & Williams, W. M. (2012). Increasing achievementand higher-education representation of under-representedgroups in science, technology, engineering, and mathematicsfields: A review of current K–12 intervention programs.Journal of Women and Minorities in Science and Engineering,18(1), 21–53.van Knippenberg, D., van Ginkel, W. P., & Homan, A. C.(2013). Diversity mindsets and the performance ofdiverse teams. Organizational Behavior and HumanDecision Processes, 121(2), 183–93. doi:10.1016/j.obhdp.2013.03.003.Van Loo, K. J., & Rydell, R. J. (2014). Negative exposure:Watching another woman subjected to dominant malebehavior during a math interaction can induce stereotypethreat. Social Psychological and Personality Science, 5(5),601–7. doi:10.1177/1948550613511501.Varma, R. (2010). Why so few women enroll in computing?Gender and ethnic differences in students’ perception.Computer Science Education, 20(4), 301–16. doi:10.1080/08993408.2010.527697.Vaz, R. F., Quinn, P., Heinricher, A. C., & Rissmiller, K. J. (2013,June). Gender differences in the long-term impacts of projectbasedlearning. Paper presented at the American Society forEngineering Education Annual Conference, Atlanta, GA.

AAUW 141von Hippel, C., Issa, M., Ma, R., & Stokes, A. (2011).Stereotype threat: Antecedents and consequences forworking women. European Journal of Social Psychology,41(2), 151–61. doi:10.1002/ejsp.749.von Hippel, C., Walsh, A.M., & Zouroudis, A. (2011).Identity separation in response to stereotype threat.Social Psychology and Personality Science, 2(3), 317–24.doi:10.1177/1948550610390391.Walton, G. M., & Cohen, G. L. (2007). A question ofbelonging: Race, social fit, and achievement. Journalof Personality and Social Psychology, 92(1), 82–96.doi:10.1037/0022-3514.92.1.82.———. (2011). A brief social-belonging intervention improvesacademic and health outcomes of minority students.Science, 331(6023), 1447–51. doi:10.1126/science.1198364.Walton, G. M., Cohen, G. L., Cwir, D., & Spencer, S. J. (2012).Mere belonging: The power of social connections. Journalof Personality and Social Psychology, 102(3), 513–32.doi:10.1037/a0025731.Walton, G. M., Logel, C., Peach, J. M, Spencer, S. J., & Zanna,M. P. (2014). Two brief interventions to mitigate a “chillyclimate” transform women’s experience, relationships,and achievement in engineering. Journal of EducationalPsychology. doi:10.1037/a0037461.Walton, G. M., & Spencer, S. J. (2009). Latent ability: Gradesand test scores systematically underestimate the intellectualability of negatively stereotyped students. PsychologicalScience, 20(9), 1132–39.Walton, G. M., Spencer, S. J., & Erman, S. (2013). Affirmativemeritocracy. Social Issues and Policy Review, 7(1), 1–35.doi:10.1111/j.1751-2409.2012.01041.x.Wang, M. T., Eccles, J. S., & Kenny, S. (2013). Not lack ofability but more choice: Individual and gender differencesin choice of careers in science, technology, engineering,and mathematics. Psychological Science, 24(5), 770–75.doi:10.1177/0956797612458937.Wei, X., Christiano, E. R. A., Yu, J. W., Blackorby, J., Shattuck,P., & Newman, L. A. (2014). Postsecondary pathways andpersistence for STEM versus non-STEM majors: Amongcollege students with an autism spectrum disorder. Journalof Autism and Developmental Disorders, 44(5), 1159–67.doi:10.1007/s10803-013-1978-5.Weisgram, E. S., & Bigler, R. S. (2006). Girls and sciencecareers: The role of altruistic values and attitudes aboutscientific tasks. Journal of Applied Developmental Psychology,27(4), 326–48. doi:10.1016/j.appdev.2006.04.004.Weisgram, E. S., Bigler, R. S., & Liben, L. S. (2010). Gender,values, and occupational interests among children, adolescents,and adults. Child Development, 81(3), 778–96.doi:10.1111/j.1467-8624.2010.01433.x.Weisgram, E. S., & Diekman, A. B. (2014). Believing that sciencehelps people predicts positivity toward science careers frommiddle school through college [Manuscript in preparation].University of Wisconsin, Stevens Point.West, T. V., Heilman, M. E., Gullet, L., Moss-Racusin, C.A., & Magee, J. C. (2012). Building blocks of bias:Gender composition predicts male and female groupmembers’ evaluations of each other and the group.Journal of Experimental Social Psychology, 48(5), 1209–12.doi:10.1016/j.jesp.2012.04.012.Williams, C. L., Muller, C., & Kilanski, K. (2012). Genderedorganizations in the new economy. Gender and Society,26(4), 549–73. doi:10.1177/0891243212445466.Williams, G. (2014). Are you sure your software is gender-neutral?Interactions, 21(1), 36–39. doi:10.1145/2524808.Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., &Malone, T. W. (2010). Evidence for a collective intelligencefactor in the performance of human groups. Science,330(6004), 686–88. doi:10.1126/science.1193147.Yoder, B. L. (2013, November). Women in engineering. ASEEPrism, 17.Young, D. M., Rudman, L. A., Buettner, H. M., & McLean, M.C. (2013). The influence of female role models on women’simplicit science cognitions. Psychology of Women Quarterly,37(3), 283–92. doi:10.1177/0361684313482109.Young, M. J. (2012). Engineering a place for women: A study ofhow departmental climate influences the career satisfactionof female mechanical engineering faculty members (Doctoraldissertation). Syracuse University, Syracuse, NY.

AAUW Educational FoundationAAUW Research ReportsRecent AAUW reports may be downloaded for free atwww.aauw.org/what-we-do/research.Women in CommunityColleges: Access toSuccess (2013)Graduating to a Pay Gap:The Earnings of Womenand Men One Year afterCollege Graduation (2012)Crossing the Line:Sexual Harassmentat School (2011)Why So Few? Womenin Science, Technology,Engineering, andMathematics (2010)Where the Girls Are:The Facts about GenderEquity in Education (2008)Behind the Pay Gap (2007)Drawing the Line: SexualHarassment on Campus(2006)Tenure Denied: Cases ofSex Discrimination inAcademia (2004)

AAUW at Workfor Women and GirlsThe American Association of University Women (AAUW) is the nation’s leadingorganization empowering women and girls. Since 1881, AAUW has helped womenmake a difference for themselves and their communities.AAUW’s mission is to advance equity for women and girls through advocacy, education,philanthropy, and research.ResearchOur rigorous reports fuel curricula, legislation,grassroots projects, and best practices.EducationOur programs reach women and girls at every ageand every stage—from K–12 to careers to retirement.PhilanthropyOne of the world’s largest funders of women’seducation, AAUW gives $3.7 million a year infellowships and grants—many of which supportwomen in science, technology, engineering, andmath.AdvocacyAAUW’s nonpartisan work keeps women’s progresson track in local, state, national, and global publicpolicy.And AAUW brings training and empowerment to thousands.10,000college women getleadership trainingfrom AAUW each year.11,000girls attend AAUWscience, technology,engineering, and mathprograms every year.1,000member leadersattend our biennialconference.AAUW boasts 170,000 members and supporters, 1,000 local branches (at least one in every state, andwe’re expanding globally), and 800 college/university partner members.Join us.www.aauw.org/joinSupport our work.www.aauw.org/contribute1111 Sixteenth St. NW • Washington, DC 20036 • 202.785.7700 • www.aauw.org

AAUW Board of DirectorsPatricia Fae Ho, AAUW PresidentJulia T. Brown, AAUW Vice PresidentRosalynd C. Homer, AAUW Finance Vice PresidentSandra Camillo, AAUW SecretaryAmy BlackwellKathryn BraemanMalinda GaulCharmen GoehringSally Anne GoodsonEileen HartmannDavid KirkwoodBetsy McDowellRebecca NorlanderPamella ThielPeggy Ryan WilliamsAAUW Executive OfficeLinda D. Hallman, Executive DirectorJill Birdwhistell, Chief Operating OfficerMike Gellman, Chief Financial OfficerMark Hopkins, Chief Strategy OfficerAAUW Research DepartmentCatherine Hill, Vice President of ResearchKathleen Benson, Research AssistantChristianne Corbett, Senior ResearcherAAUW Art, Editorial, and Media DepartmentRebecca Lanning, Vice President of Art, Editorial, and MediaHannah Moulton Belec, Senior Editor/WriterKathryn Bibler, Editor/WriterElizabeth Bolton, Associate Director of Art, Editorial, and MediaKatherine Broendel, Media and Public Relations ManagerMukti Desai, Creative DirectorElizabeth Escobar, Editorial AssistantLisa Goodnight, Senior Media and Public Relations ManagerCasey Riney, Junior DesignerAllison VanKanegan, Senior Graphic DesignerAAUW Development DepartmentCordy Galligan, Vice President of Marketing and Business DevelopmentEllen Root, Vice President of Corporate DevelopmentBethany Imondi, Development AssociateNicole Phillips, Senior Associate of Corporate Development

MissionAAUW advances equity for women and girls throughadvocacy, education, philanthropy, and research.Value PromiseBy joining AAUW, you belong to a community thatbreaks through educational and economic barriersso that all women and girls have a fair chance.Vision StatementAAUW empowers all women and girls to reach their highest potential.Diversity StatementAAUW values and seeks a diverse membership. There shall be nobarriers to full participation in this organization on the basis of gender,race, creed, age, sexual orientation, national origin, disability, or class.

1111 Sixteenth St. NW • Washington, DC 20036202.785.7700 • connect@aauw.orgwww.aauw.org